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Depeursinge Adrien

Professeur-e HES Ordinaire

Contrat principal

Professeur-e HES Ordinaire

Bureau: TP107

HES-SO Valais-Wallis - Haute Ecole de Gestion
Route de la Plaine 2, Case postale 80, 3960 Sierre, CH

Domaine
Economie et services

Filière principale
Informatique de gestion

Research Interests

Personalized medicine via image-based tissue analysis and machine learning for cancer and other diseases (Radiomics)
Building digital models of disease-specific radiological phenotypes at the organ level using localization systems and texture-based computational models of biomedical tissue.
N-dimensional texture analysis with control of scales and orientations
Multimodal fusion
Clinical workflows of image-based computer-aided systems
Web-based tools for image-based personalized medicine research (https://quantimage2.ehealth.hevs.ch/)

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2024

Making sense of radiomics :

Article scientifique ArODES

insights on human–AI collaboration in medical interaction from an observational user study

Jakub Mlynár, Adrien Depeursinge, John O. Prior, Roger Schaer, Alexandre Martroye de Joly, Florian Evéquoz

Frontiers in Communication, 2024, 8

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Résumé:

Technologies based on “artificial intelligence” (AI) are transforming every part of our society, including healthcare and medical institutions. An example of this trend is the novel field in oncology and radiology called radiomics, which is the extracting and mining of large-scale quantitative features from medical imaging by machine-learning (ML) algorithms. This paper explores situated work with a radiomics software platform, QuantImage (v2), and interaction around it, in educationally framed hands-on trial sessions where pairs of novice users (physicians and medical radiology technicians) work on a radiomics task consisting of developing a predictive ML model with a co-present tutor. Informed by ethnomethodology and conversation analysis (EM/CA), the results show that learning about radiomics more generally and learning how to use this platform specifically are deeply intertwined. Common-sense knowledge (e.g., about meanings of colors) can interfere with the visual representation standards established in the professional domain. Participants' skills in using the platform and knowledge of radiomics are routinely displayed in the assessment of performance measures of the resulting ML models, in the monitoring of the platform's pace of operation for possible problems, and in the ascribing of independent actions (e.g., related to algorithms) to the platform. The findings are relevant to current discussions about the explainability of AI in medicine as well as issues of machinic agency.

2023

Wide kernels and their DCT compression in convolutional networks for nuclei segmentation

Article scientifique ArODES

Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge

Informatics in Medicine Unlocked, 43, 101403

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Résumé:

The locality and spatial field of view of image operators have played a major role in image analysis, from hand-crafted to deep learning methods. In Convolutional Neural Networks (CNNs), the field of view is traditionally set to very small values (e.g. 3 × 3 pixels) for individual kernels and grown throughout the network by cascading layers. Automatically learning or adapting the best spatial support of the kernels can be done by using large kernels. Due to the computation requirements of standard CNN architectures, this has been little investigated in the literature. However, if large receptive fields are needed to capture wider contextual information on a given task, it could be learned from the data. Obtaining an optimal receptive field with few layers is very relevant in applications with a limited amount of annotated training data, e.g. in medical imaging. We show that CNNs (2D U-Nets) with large kernels outperform similar models with standard small kernels on the task of nuclei segmentation in histopathology images. We observe that the large kernels mostly capture low-frequency information, which motivates the need for large kernels and their efficient compression via the Discrete Cosine Transform (DCT). Following this idea, we develop a U-Net model with wide and compressed DCT kernels that leads to similar performance and trends to the standard U-Net, with reduced complexity. Visualizations of the kernels in the spatial and frequency domains, as well as the effective receptive fields, provide insights into the models’ behaviors and the learned features.

How far MS lesion detection and segmentation are integrated into the clinical workflow? :

Article scientifique ArODES

a systematic review

Frederico Spagnolo, Adrien Depeursinge, Sabine Schädelin, Aysenur Akbulut, Henning Müller, Muhamed Barakovic, Lester Melie-Garcia, Meritxell Bach Cuadra, Cristina Granziera

NeuroImage: Clinical, 39, 103491

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Résumé:

Introduction: Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI). Aims: Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow. Methods: Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI’s six-steps, which include a tool’s technical assessment, clinical validation, and integration. Results: We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth. Conclusions: To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients’ management of such tools remain almost unexplored.

QuantImage v2 :

Article scientifique ArODES

a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

Daniel Abler, Roger Schaer, Valentin Oreiller, Himanshu Verma, Julien Reichenbach, Orfeas Aidonopoulos, Florian Evéquoz, Mario Jreige, John O. Prior, Adrien Depeursinge

European Radiology Experimental, 7, 16

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Résumé:

Background : Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. Methods We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. Results : Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports “no-code” development and evaluation of machine learning models against patient-specific outcome data. Conclusions : QI2 fills a gap in the radiomics software landscape by enabling “no-code” radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/. Key points: - As domain experts, physicians play a key role in the development of radiomics models. - Existing software solutions do not support physician-driven research optimally. - QuantImage v2 implements a physician-centered vision for radiomics research. - QuantImage v2 is a web-based, “no-code” radiomics research platform.

3D-printed iodine-ink CT phantom for radiomics feature extraction :

Article scientifique ArODES

advantages and challenges

Michael Bach, Christoph Aberle, Adrien Depeursinge, Oscar Jimenez-del-Toro, Robert Schaer, Kyriakos Flouris, Ender Konukoglu, Henning Müller, Bram Stieltjes, Markus M. Obmann

Medical Physics, 50, 9, 5682-5697

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Résumé:

Background : To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D-printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. Purpose: To present the design process of an anthropomorphic 3D-printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's X-ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. Methods : Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26 × 19 × 30 cm3. This DICOM dataset was printed on paper using iodinated ink. The phantom was CT-scanned and compared to the original image dataset used for printing the phantom. The water-equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual-energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. Results : Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has X-ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid-frequencies (e.g., for ω = 0.3942 mm−1, |f(ω)|original = 0.09 * |fmax|original and |f(ω)|phantom = 0.12 * |fmax|phantom). Conclusions : 3D-iodine-ink-printing technology can be used to print anthropomorphic phantoms with a water-equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual-energy CT analyses.

2022

Quality assessment for interoperable quantitative CT imaging (QA4IQI) - Open Access to standardized quantitative imaging

Rapport ArODES

final report - open data project call

Adrien Depeursinge, Roger Schaer

2022, Sierre : HES-SO Valais-Wallis, 11 p.

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Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT

Article scientifique ArODES

Daphné Faist, Mario Jreige, Valentin Oreiller, Marie Nicod Lalonde, Niklaus Schaefer, Adrien Depeursinge, John O. Prior

European journal of hybrid imaging, 2022, vol. 6, no. 33

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Résumé:

Background : Quality and reproducibility of radiomics studies are essential requirements for the standardisation of radiomics models. As recent data-driven respiratory gating (DDG) [18F]-FDG has shown superior diagnostic performance in lung cancer, we evaluated the impact of DDG on the reproducibility of radiomics features derived from [18F]-FDG PET/CT in comparison to free-breathing flow (FB) imaging. Methods : Twenty four lung nodules from 20 patients were delineated. Radiomics features were derived on FB flow PET/CT and on the corresponding DDG reconstruction using the QuantImage v2 platform. Lin’s concordance factor (Cb) and the mean difference percentage (DIFF%) were calculated for each radiomics feature using the delineated nodules which were also classified by anatomical localisation and volume. Non-reproducible radiomics features were defined as having a bias correction factor Cb < 0.8 and/or a mean difference percentage DIFF% > 10. Results : In total 141 features were computed on each concordance analysis, 10 of which were non-reproducible on all pulmonary lesions. Those were first-order features from Laplacian of Gaussian (LoG)-filtered images (sigma = 1 mm): Energy, Kurtosis, Minimum, Range, Root Mean Squared, Skewness and Variance; Texture features from Gray Level Cooccurence Matrix (GLCM): Cluster Prominence and Difference Variance; First-order Standardised Uptake Value (SUV) feature: Kurtosis. Pulmonary lesions located in the superior lobes had only stable radiomics features, the ones from the lower parts had 25 non-reproducible radiomics features. Pulmonary lesions with a greater size (defined as long axis length > median) showed a higher reproducibility (9 non-reproducible features) than smaller ones (20 non-reproducible features). Conclusion : Calculated on all pulmonary lesions, 131 out of 141 radiomics features can be used interchangeably between DDG and FB PET/CT acquisitions. Radiomics features derived from pulmonary lesions located inferior to the superior lobes are subject to greater variability as well as pulmonary lesions of smaller size.

A global taxonomy of interpretable AI :

Article scientifique ArODES

unifying the terminology for the technical and social sciences

Mara Graziani, Lidia Dutkiewicz, Davide Calvaresi, José Pereira Amorim, Katerina Yordanova, Mor Vered, Rahul Nair, Pedro Henriques Abreu, Tobias Blanke, Valeria Pulignano, John O. Prior, Lode Lauwaert, Wessel Reijers, Adrien Depeursinge, Vincent Andrearczyk, Henning Müller

Artificial intelligence review, To be published

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Résumé:

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are “weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a—highly needed—standard for the communication among interdisciplinary areas of AI.

Steer’n’Detect :

Article scientifique ArODES

fast 2D template detection with accurate orientation estimation

Virginie Uhlmann, Zsuzsanna Püspöki, Adrien Depeursinge, Michael Unser, Daniel Sage, Julien Fageot

Bioinformatics, 2022, vol. 38, no. 11, pp. 3146–3148

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Résumé:

Rotated template matching is an efficient and versatile algorithm to analyze microscopy images, as it automates the detection of stereotypical structures, such as organelles that can appear at any orientation. Its performance however quickly degrades in noisy image data. We introduce Steer’n’Detect, an ImageJ plugin implementing a recently published algorithm to detect patterns of interest at any orientation with high accuracy from a single template in 2D images. Steer’n’Detect provides a faster and more robust substitute to template matching. By adapting to the statistics of the image background, it guarantees accurate results even in the presence of noise. The plugin comes with an intuitive user interface facilitating results analysis and further post-processing.

Assessing radiomics feature stability with simulated CT acquisitions

Article scientifique ArODES

Kyriakos Flouris, Oscar Alfonso Jiménez del Toro, Christoph Aberle, Michael Bach, Roger Schaer, Markus M. Obmann, Bram Stieltjes, Henning Müller, Adrien Depeursinge, Ender Konukoglu

Scientific Reports, 2022, vol. 12, article 4732, pp. 1-11

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Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis and research. Tissue characterisation is improved via the “radiomics” features, whose extraction can be automated. Despite the advances, stability of quantitative features remains an important open problem. As features can be highly sensitive to variations of acquisition details, it is not trivial to quantify stability and efficiently select stable features. In this work, we develop and validate a Computed Tomography (CT) simulator environment based on the publicly available ASTRA toolbox (www.astra-toolbox.com). We show that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images generated by the simulator are similar to those observed in a tandem phantom study. Additionally, we show that the variability is matched between a multi-center phantom study and simulated results. Consequently, we demonstrate that the simulator can be utilised to assess radiomics features’ stability and discriminative power.

Cleaning radiotherapy contours for radiomics studies, is it worth it ? :

Article scientifique ArODES

a head and neck cancer study

Pierre Fontaine, Vincent Andrearczyk, Valentin Oreiller, Daniel Abler, Joël Castelli, Oscar Acosta, Renaud De Crevoisier, Martin Vallières, Mario Jreige, John O. Prior, Adrien Depeursinge

Clinical and translational radiation oncology, 2022, vol. 33, pp. 153-158

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Résumé:

A vast majority of studies in the radiomics field are based on contours originating from radiotherapy planning. This kind of delineation (e.g. Gross Tumor Volume, GTV) is often larger than the true tumoral volume, sometimes including parts of other organs (e.g. trachea in Head and Neck, H&N studies) and the impact of such over-segmentation was little investigated so far. In this paper, we propose to evaluate and compare the performance between models using two contour types: those from radiotherapy planning, and those specifically delineated for radiomics studies. For the latter, we modified the radiotherapy contours to fit the true tumoral volume. The two contour types were compared when predicting Progression-Free Survival (PFS) using Cox models based on radiomics features extracted from FluoroDeoxyGlucose-Positron Emission Tomography (FDG-PET) and CT images of 239 patients with oropharyngeal H&N cancer collected from five centers, the data from the 2020 HECKTOR challenge. Using Dedicated contours demonstrated better performance for predicting PFS, where Harell’s concordance indices of 0.61 and 0.69 were achieved for Radiotherapy and Dedicated contours, respectively. Using automatically Resegmented contours based on a fixed intensity range was associated with a C-index of 0.63. These results illustrate the importance of using clean dedicated contours that are close to the true tumoral volume in radiomics studies, even when tumor contours are already available from radiotherapy treatment planning

2021

FDG-PET/CT-based prognostic survival model after surgery for head and neck cancer

Article scientifique ArODES

Gwenaelle Creff, Franck Jegoux, Xavier Palard, Adrien Depeursinge, Ronan Abgral, Remi Marianowski, Jean-Christophe Leclere, Thomas Eugene, Olivier Malard, Anne Devillers, Joel Castelli

Journal of nuclear medicine, 2022, vol. 63, no. 5

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Rationale: The aims of this multicenter study were to identify clinical and preoperative PET/CT parameters predicting Overall Survival (OS) and Distant Metastasis Free Survival(DMFS) from a cohort of Head and Neck Squamous Cell Carcinoma (HNSCC) patients treated with surgery, to generate a prognostic model of OS and DMFS and to validate this prognostic model with an independent cohort. Materials and Methods: A total of 382 consecutive HNSCC patients divided into training (n = 318) and validation cohorts (n = 64) were retrospectively included. The following PET/CT parameters were analyzed: clinical parameters, SUVmax, SUVMean, Metabolic Tumor Volume (MTV), Total Lesion Glycolysis (TLG) and distance parameters for the primary tumor and lymph nodes defined by two segmentation methods (relative SUVmax threshold and absolute SUV threshold). Cox analyses were performed for OS and DMFS in the training cohort. The c-index was used to identify highly prognostic parameters. These prognostic parameters were externally tested in the validation cohort. Results: In multivariable analysis, the significant parameters for OS were T stage and Nodal-MTV, achieving a c-index of 0.64 (p<0.001). For DMFS, the significant parameters were T stage, Nodal-MTV and maximal tumor-node distance, with a c-index of 0.76 (p<0.001). These combinations of parameters were externally validated, achieving c-indices of 0.63 (p<0.001) and 0.71 (p<0.001) for OS and DMFS, respectively. Conclusion: The Nodal MTV associated with maximal distance between the primary tumor and the lymph node was significantly correlated with the risk of DMFS. Moreover, this parameter in addition to clinical parameters was associated with higher risk of death. These prognostic factors may be used to tailor individualized treatment.

The discriminative power and stability of radiomics features with computed tomography variations :

Article scientifique ArODES

task-based analysis in an anthropomorphic 3D-printed CT phantom

Oscar Alfonso Jiménez del Toro, Christoph Aberle, Michael Bach, Roger Schaer, Markus Obmann, Kyriakos Flouris, Ender Konukoglu, Bram Stieltjes, Henning Müller, Adrien Depeursinge

Investigative radiology, 2021, vol. 56, no. 12, pp. 820-825

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Objectives : The aims of this study were to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data. Materials and Methods : A radiopaque 3D phantom was developed using real patient data and a potassium iodide solution paper-printing technique. Normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom. The stability and discriminative power of 86 radiomics features were assessed in measurements taken from 240 CT series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing. Pairwise parameter group and pairwise tissue class comparisons were performed using Wilcoxon signed rank tests. Results : In total, 19,264 feature stability tests and 8256 discriminative power tests were performed. The 8 CT parameter variation pairwise group comparisons had statistically significant differences on average in 78/86 radiomics features. On the other hand, 84% of the univariate radiomics feature tests had a successful and statistically significant differentiation of the 4 classes of liver tissue. The 86 radiomics features were ranked according to the cumulative sum of successful stability and discriminative power tests. Conclusions : The differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from CT parameter variations.

Head and neck tumor segmentation in PET/CT :

Article scientifique ArODES

The HECKTOR challenge

Valentin Oreiller, Vincent Andrearczyk, Mario Jreige, Sarah Boughdad, Hesham Elhalawanic, Joel Castelli, Martin Vallières, Simeng Zhu, Juanying Xie, Ying Peng, Andrei Iantsen, Matthieu Hatt, Yading Yuan, Jun Ma, Xiaoping Yang, Chinmay Rao, Suraj Pai, Kanchan Ghimire, Xue Feng, Mohamed A. Naser, Clifton D. Fuller, Fereshteh Yousefirizi, Arman Rahmim, Huai Chen, Lisheng Wang, John O. Prior, Adrien Depeursinge

Medical image analysis, 2022, vol. 77, article no. 102336, pp. 1-16

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Résumé:

This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge’s task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (H&N) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in H&N cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs.

On the scale invariance in state of the art CNNs trained on ImageNet

Article scientifique ArODES

Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge, Vincent Andrearczyk

Machine learning and knowledge extraction, 2021, vol. 3, no. 2, pp. 374-391

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The diffused practice of pre-training Convolutional Neural Networks (CNNs) on large natural image datasets such as ImageNet causes the automatic learning of invariance to object scale variations. This, however, can be detrimental in medical imaging, where pixel spacing has a known physical correspondence and size is crucial to the diagnosis, for example, the size of lesions, tumors or cell nuclei. In this paper, we use deep learning interpretability to identify at what intermediate layers such invariance is learned. We train and evaluate different regression models on the PASCAL-VOC (Pattern Analysis, Statistical modeling and ComputAtional Learning-Visual Object Classes) annotated data to (i) separate the effects of the closely related yet different notions of image size and object scale, (ii) quantify the presence of scale information in the CNN in terms of the layer-wise correlation between input scale and feature maps in InceptionV3 and ResNet50, and (iii) develop a pruning strategy that reduces the invariance to object scale of the learned features. Results indicate that scale information peaks at central CNN layers and drops close to the softmax, where the invariance is reached. Our pruning strategy uses this to obtain features that preserve scale information. We show that the pruning significantly improves the performance on medical tasks where scale is a relevant factor, for example for the regression of breast histology image magnification. These results show that the presence of scale information at intermediate layers legitimates transfer learning in applications that require scale covariance rather than invariance and that the performance on these tasks can be improved by pruning off the layers where the invariance is learned. All experiments are performed on publicly available data and the code is available on GitHub.

Principled design and implementation of steerable detectors

Article scientifique ArODES

Julien Fageot, Virginie Uhlmann, Zsuzsanna Püspöki, Benjamin Beck, Michael Unser, Adrien Depeursinge

IEEE transactions on image processing, 2021, vol. 30, pp. 4465-4478

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Résumé:

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown positions and orientations that we aim at retrieving. We propose a continuous-domain additive image model, where the analyzed image is the sum of the patterns to localize and a background with self-similar isotropic power-spectrum. We are then able to compute the optimal filter fulfilling the SNR criterion based on one single template and background pair: it strongly responds to the template while being optimally decoupled from the background model. In addition, we constrain our filter to be steerable, which allows for a fast template detection together with orientation estimation. In practice, the implementation requires to discretize a continuous-domain formulation on polar grids, which is performed using quadratic radial B-splines. We demonstrate the practical usefulness of our method on a variety of template approximation and pattern detection experiments. We show that the detection performance drastically improves when we exploit the statistics of the background via its power-spectrum decay, which we refer to as spectral-shaping. The proposed scheme outperforms state-of-the-art steerable methods by up to 50% of absolute detection performance.

2020

The importance of feature aggregation in radiomics :

Article scientifique ArODES

a head and neck cancer study

Pierre Fontaine, Oscar Acosta, Joël Castelli, Renaud De Crevoisier, Henning Müller, Adrien Depeursinge

Scientific reports, 2020, vol. 10, article no. 19679 , pp. 1-11

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Résumé:

In standard radiomics studies the features extracted from clinical images are mostly quantified with simple statistics such as the average or variance per Region of Interest (ROI). Such approaches may smooth out any intra-region heterogeneity and thus hide some tumor aggressiveness that may hamper predictions. In this paper we study the importance of feature aggregation within the standard radiomics workflow, which allows to take into account intra-region variations. Feature aggregation methods transform a collection of voxel values from feature response maps (over a ROI) into one or several scalar values that are usable for statistical or machine learning algorithms. This important step has been little investigated within the radiomics workflows, so far. In this paper, we compare several aggregation methods with standard radiomics approaches in order to assess the improvements in prediction capabilities. We evaluate the performance using an aggregation function based on Bags of Visual Words (BoVW), which allows for the preservation of piece-wise homogeneous information within heterogeneous regions and compared with standard methods. The different models are compared on a cohort of 214 head and neck cancer patients coming from 4 medical centers. Radiomics features were extracted from manually delineated tumors in clinical PET-FDG and CT images were analyzed. We compared the performance of standard radiomics models, the volume of the ROI alone and the BoVW model for survival analysis. The average concordance index was estimated with a five fold cross-validation. The performance was significantly better using the BoVW model 0.627 (95% CI: 0.616–0.637) as compared to standard radiomics0.505 (95% CI: 0.499–0.511), mean-var. 0.543 (95% CI: 0.536–0.549), mean0.547 (95% CI: 0.541–0.554), var.0.530 (95% CI: 0.524–0.536) or volume 0.577 (95% CI: 0.571–0.582). We conclude that classical aggregation methods are not optimal in case of heterogeneous tumors. We also showed that the BoVW model is a better alternative to extract consistent features in the presence of lesions composed of heterogeneous tissue.

Local rotation invariance in 3D CNNs

Article scientifique ArODES

Vincent Andrearczyk, Julien Fageot, Valentin Oreiller, Xavier Montet, Adrien Depeursinge

Medical image analysis, 2020, vol. 65, article 101756, pp. 1-11

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Résumé:

Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations. LRI constituted the cornerstone of several breakthroughs in texture analysis, including Local Binary Patterns (LBP), Maximum Response 8 (MR8) and steerable filterbanks. Whereas globally rotation invariant Convolutional Neural Networks (CNN) were recently proposed, LRI was very little investigated in the context of deep learning. LRI designs allow learning filters accounting for all orientations, which enables a drastic reduction of trainable parameters and training data when compared to standard 3D CNNs. In this paper, we propose and compare several methods to obtain LRI CNNs with directional sensitivity. Two methods use orientation channels (responses to rotated kernels), either by explicitly rotating the kernels or using steerable filters. These orientation channels constitute a locally rotation equivariant representation of the data. Local pooling across orientations yields LRI image analysis. Steerable filters are used to achieve a fine and efficient sampling of 3D rotations as well as a reduction of trainable parameters and operations, thanks to a parametric representations involving solid Spherical Harmonics (SH),which are products of SH with associated learned radial profiles. Finally, we investigate a third strategy to obtain LRI based on rotational invariants calculated from responses to a learned set of solid SHs. The proposed methods are evaluated and compared to standard CNNs on 3D datasets including synthetic textured volumes composed of rotated patterns, and pulmonary nodule classification in CT. The results show the importance of LRI image analysis while resulting in a drastic reduction of trainable parameters, outperforming standard 3D CNNs trained with rotational data augmentation.

Impact of a Gaussian filter applied to post-reconstruction PET on radiomic features in assessing tumor heterogeneity in breast cancer

Article scientifique ArODES

Sarah Boughdad, Valentin Oreiller, Marie Meyer, Niklaus Schaefer, Marie Nicod-Lalonde, Mario Jreige, Adrien Depeursinge, John O. Prior

Journal of nuclear medicine, 2020, vol. 61, no. supplement 1, article 612

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The image biomarker standardization initiative :

Article scientifique ArODES

standardized quantitative radiomics for high-throughput image-based phenotyping

Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah, Hugo J. W. L. Aerts, Vincent Andrearczyk, Adrien Depeursinge, Henning Müller

Radiology, 2020, vol. 295, no. 2, pp. 328-338

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Résumé:

The Image Biomarker Standardization Initiative validated consensus-based reference values for 169 radiomics features, thus enabling calibration and verification of radiomics software. Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software.

Impact of a Gaussian filter applied to post-reconstruction PET images on radiomic features to predict complete pathological response in breast cancer

Article scientifique ArODES

Sarah Boughdad, Valentin Oreiller, Marie Meyer, Niklaus Schaefer, Marie Nicod-Lalonde, Mario Jreige, Adrien Depeursinge, John O. Prior

Journal of nuclear medicine, 2020, vol. 61, no. supplement 1, article 606

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Evaluation of the prognostic value of FDG PET/CT parameters for patients with surgically treated head and neck cancer

Article scientifique ArODES

Gwenaelle Creff, Anne Deviller, Adrien Depeursinge, Xavier Palard-Novello, Oscar Acosta, Franck Jejoux, Joel Castelli

JAMA Otolaryngology–Head Neck Surgery, 2020, vol. 146, No. 5, pp. 471-479

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Résumé:

Objective : To report the available evidence on the value of quantitative parameters of fluorodeoxyglucose F 18–labeled positron emission tomography and computed tomography (FDG-PET/CT) performed before surgical treatment of HNSCC to estimate overall survival (OS), disease-free survival (DFS), and distant metastasis (DM) and to discuss their limitations.

Integrating radiomics into holomics for personalised oncology :

Article scientifique ArODES

from algorithms to bedside

Roberto Gatta, Adrien Depeursinge, Osman Ratib, Olivier Michielin, Antoine Leimgruber

European radiology experimental, 2020, vol. 4, article 11, pp. 1-9

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Résumé:

Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other “omics” data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other “omics” into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called “holomics”, will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists.

2019

Texture-driven parametric snakes for semi-automatic image segmentation

Article scientifique ArODES

Anaïs Badoual, Michael Unser, Adrien Depeursinge

Computer vision and image understanding, 2019, vol. 188, article 102793, pp. 1-11

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Résumé:

We present a texture-driven parametric snake for semi-automatic segmentation of a single and closed structure in an image. We propose a new energy functional that combines intensity and texture information. The two types of image information are balanced using Fisher’s linear discriminant analysis. The framework can be used with any filter-based texture features. The parametric representation of the snake allows for easy and friendly user interaction while the framework can be trained on-the-fly from pixel collections provided by the user. We demonstrate the efficiency of the snake through an extensive validation on synthetic as well as on real data. Additionally, we show that the proposed snake is robust to noise and that it improves the segmentation performance when compared to an intensity-only scheme.

Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification

Article scientifique ArODES

Ranveer Joyseeree, Sebastian Otálora, Henning Müller, Adrien Depeursinge

Medical image analysis, August 2019, vol. 56, pp. 172-183

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Résumé:

A novel method to detect and classify several classes of diseased and healthy lung tissue in CT (Computed Tomography), based on the fusion of Riesz and deep learning features, is presented. First, discriminative parametric lung tissue texture signatures are learned from Riesz representations using a one–versus–one approach. The signatures are generated for four diseased tissue types and a healthy tissue class, all of which frequently appear in the publicly available Interstitial Lung Diseases (ILD) dataset used in this article. Because the Riesz wavelets are steerable, they can easily be made invariant to local image rotations, a property that is desirable when analyzing lung tissue micro–architectures in CT images. Second, features from deep Convolutional Neural Networks (CNN) are computed by fine–tuning the Inception V3 architecture using an augmented version of the same ILD dataset. Because CNN features are both deep and non–parametric, they can accurately model virtually any pattern that is useful for tissue discrimination, and they are the de facto standard for many medical imaging tasks. However, invariance to local image rotations is not explicitly implemented and can only be approximated with rotation–based data augmentation. This motivates the fusion of Riesz and deep CNN features, as the two techniques are very complementary. The two learned representations are combined in a joint softmax model for final classification, where early and late feature fusion schemes are compared. The experimental results show that a late fusion of the independent probabilities leads to significant improvements in classification performance when compared to each of the separate feature representations and also compared to an ensemble of deep learning approaches.

Neural network training for cross-protocol radiomic feature standardization in computed tomography

Article scientifique ArODES

Vincent Andrearczyk, Adrien Depeursinge, Henning Müller

Journal of Medical Imaging, 2019, vol. 6, no. 2

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Résumé:

Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, pixel spacing, acquisition protocol, and reconstruction parameters. We propose and compare two methods to transform quantitative image features in order to improve their stability across varying image acquisition parameters while preserving the texture discrimination abilities. In this way, variations in extracted features are representative of true physiopathological tissue changes in the scanned patients. A first approach is based on a two-layer neural network that can learn a nonlinear standardization transformation of various types of features including handcrafted and deep features. Second, domain adversarial training is explored to increase the invariance of the transformed features to the scanner of origin. The generalization of the proposed approach to unseen textures and unseen scanners is demonstrated by a set of experiments using a publicly available computed tomography texture phantom dataset scanned with various imaging devices and parameters.

PET-based prognostic survival model after radiotherapy for head and neck cancer :

Article scientifique ArODES

responsibility, individual consumption and demand-side policies in European deep decarbonisation pathways

Joël Castelli, Adrien Depeursinge, A Devillers, B. Campillo-Gimenez, Y Dicente, J. O. Prior, E. Chajon, F. Jegoux, C. Sire, O. Acosta, E. Gherga, X. Sun, B. De Bari, R. de Crevoisier

European journal of nuclear medicine and molecular imaging, March 2019, vol. 46, no. 3, pp. 638–649

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Résumé:

Purpose The aims of this multicentre retrospective study of locally advanced head and neck cancer (LAHNC) treated with definitive radiotherapy were to (1) identify positron emission tomography (PET)- 18 F-fluorodeoxyglucose ( 18 F-FDG) parameters correlated with overall survival (OS) in a training cohort, (2) compute a prognostic model, and (3) externally validate this model in an independent cohort. Materials and methods A total of 237 consecutive LAHNC patients divided into training ( n = 127) and validation cohorts ( n = 110) were retrospectively analysed. The following PET parameters were analysed: SUV Max , metabolic tumour volume (MTV), total lesion glycolysis (TLG), and SUV Mean for the primary tumour and lymph nodes using a relative SUV Max threshold or an absolute SUV threshold. Cox analyses were performed on OS in the training cohort. The c-index was used to identify the highly prognostic parameters. A prognostic model was subsequently identified, and a nomogram was generated. The model was externally tested in the validation cohort. Results In univariate analysis, the significant PET parameters for the primary tumour included MTV (relative thresholds from 6 to 83% and absolute thresholds from 1.5 to 6.5) and TLG (relative thresholds from 1 to 82% and absolute thresholds from 0.5 to 4.5). For the lymph nodes, the significant parameters included MTVand TLG regardless of the threshold value. In multivariate analysis, tumour site, p16 status, MTV35% of the primary tumour, and MTV44% of the lymph nodes were independent predictors of OS. Based on these four parameters, a prognostic model was identified with a c-index of 0.72. The corresponding nomogram was generated. This prognostic model was externally validated, achieving a c-index of 0.66. Conclusions A prognostic model of OS based on primary tumour and lymph node MTV, tumour site, and p16 status was proposed and validated. The corresponding nomogram may be used to tailor individualized treatment

Revealing tumor habitats from texture heterogeneity analysis for classification of lung cancer malignancy and aggressiveness

Article scientifique ArODES

Dmitri Cherezo, Dmitri Goldgof, Lawrence Hall, Robert Gillies, Matthew Schabath, Henning Müller, Adrien Depeursinge

Scientific reports, Mars 2019, vol. 9, article no. 4500

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Résumé:

We propose an approach for characterizing structural heterogeneity of lung cancer nodules using Computed Tomography Texture Analysis (CTTA). Measures of heterogeneity were used to test the hypothesis that heterogeneity can be used as predictor of nodule malignancy and patient survival. To do this, we use the National Lung Screening Trial (NLST) dataset to determine if heterogeneity can represent differences between nodules in lung cancer and nodules in non-lung cancer patients. 253 participants are in the training set and 207 participants in the test set. To discriminate cancerous from non-cancerous nodules at the time of diagnosis, a combination of heterogeneity and radiomic features were evaluated to produce the best area under receiver operating characteristic curve (AUROC) of 0.85 and accuracy 81.64%. Second, we tested the hypothesis that heterogeneity can predict patient survival. We analyzed 40 patients diagnosed with lung adenocarcinoma (20 short-term and 20 long-term survival patients) using a leave-one-out cross validation approach for performance evaluation. A combination of heterogeneity features and radiomic features produce an AUROC of 0.9 and an accuracy of 85% to discriminate long- and short-term survivors.

2018

Rotation invariance and directional sensitivity :

Chapitre de livre ArODES

spherical harmonics versus radiomics features

Adrien Depeursinge, Julien Fageot, Vincent Andrearczyk, John Paul Ward, Michael Unser

Dans Liu, Minigxia, Shi, Yinghuan, Suk, Heung-Il, Machine learning in medical imaging (pp. 107-115). 2018, Cham : Springer

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Résumé:

We define and investigate the Local Rotation Invariance (LRI) and Directional Sensitivity (DS) of radiomics features. Most of the classical features cannot combine the two properties, which are antagonist in simple designs. We propose texture operators based on spherical harmonic wavelets (SHW) invariants and show that they are both LRI and DS. An experimental comparison of SHW and popular radiomics operators for classifying 3D textures reveals the importance of combining the two properties for optimal pattern characterization.

(18 F)-FDG PET/CT parameters to predict survival and recurrence in patients with locally advanced cervical cancer treated with chemoradiotherapy

Article scientifique ArODES

N. Scher, J. Castelli, Adrien Depeursinge, J. Bourhis, J. O. Prior, F. G. Herrera, M. Ozsahin

Cancer/Radiothérapie, May 2018, vol. 22, no. 3, pp. 229-235

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Résumé:

Objectif de l’étude Les objectifs de ce travail étaient d’identifier, pour des cancers du col utérin localement évolués pris en charge par chimioradiothérapie, les paramètres quantitatifs de tomographie par émission de positons prédictifs du contrôle locorégional, de la survie sans récidive et de la survie globale. Patientes et méthode Trente-sept patientes, avec un suivi médian de 52 mois, ont été incluses dans cette étude rétrospective. En plus de la standard uptake value maximale (SUVmax), les paramètres de tomographie par émission de positons (TEP) de volume (volume métabolique tumoral et indice de glycolyse lésionnelle globale) ont été calculés en utilisant différentes valeurs de seuillage en valeur absolue (de 0 à 20) ou relative (de 0 à 100 % de la SUVMax). Un nouveau paramètre de distance tumorale métabolique combinant le volume métabolique tumoral ganglionnaire avec la distance entre la tumeur primitive et chaque ganglion atteint a également été défini. L’ensemble des paramètres cliniques et de TEP a été analysé et une corrélation avec la survie globale, la survie sans récidive et le contrôle local a été recherchée. Un modèle pronostique de survie globale a été défini. Une validation interne a été réalisée par validation croisée. Résultats En analyse multifactorielle l’indice de glycolyse lésionnelle globale de la tumeur et la distance tumorale métabolique étaient les deux facteurs pronostiques indépendants prédisant la survie globale (p < 0,01), la survie sans rédicive (p < 0,01) et le contrôle locorégionale (p = 0,046). Après validation croisée, les c-index étaient pour la survie globale, la survie sans récidive et le contrôle locorégional respectivement de 0,63, 0,68 et 0,66. Un score prédictif a été calculé, permettant d’identifier deux groupes à risque. Les probabilités de survie globale, de survie sans récidive et de contrôle locorégionale à 3 ans étaient de 88 %, 78 % et 84 % pour le groupe à bas risque contre 45 %, 33 % et 38 % pour le groupe à haut risque (p < 0,01). Conclusions L’indice de glycolyse lésionnelle globale tumorale et la distance entre la tumeur primitive et chaque ganglion atteint, sont des paramètres pronostiques de la survie après chimioradiothérapie pour des cancers du col utérin. Ces paramètres pourraient permettre une identification précoce des patientes atteintes de cancer de pronostic défavorable, candidat potentiel à une intensification thérapeutique. Une validation externe prospective sera nécessaire pour confirmer ces résultats.

Rotation-covariant tissue analysis for interstitial lung diseases using learned steerable filters :

Article scientifique ArODES

performance evaluation and relevance for diagnostic aid

Ranveer Joyseeree, Henning Müller, Adrien Depeursinge

Computerized medical imaging and graphics, March 2018, vol. 64, pp. 1-11

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Résumé:

A novel method to detect and classify several classes of diseased and healthy lung tissue of interstitial lung diseases is presented, as these diseases are hard to diagnose and differentiate. Local organizations of image directions at several scales drive the process of creating discriminative lung tissue texture signatures using spatial and Fourier domain information extracted from the images. The signatures are generated for four diseased tissue classes and healthy tissue, all of which appear in the Interstitial Lung Disease (ILD) database, using a novel one-versus-one approach for learning discriminative filter signatures. A multiclass tissue classification accuracy of 80.31% is observed using Radial Basis Function (RBF) Support Vector Machines (SVMs). The presented method compares well against a variety of state-of-the-art approaches. Another strong feature of our approach is the ability to access the individual class probabilities before a final classification decision is made. This enables an analysis of the causes of misclassification in this paper. We also make the case against total reliance on the accuracy of the ground truth given that the ILD database only contains a single label for a specific region and sometimes more than one pattern can be present, particularly for regions classified as healthy tissue. Measures to address misclassifications in this context are also proposed.

2017

Signature of survival :

Article scientifique ArODES

a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma

Paul Blanc-Durand, Axel Van Der Gucht, Mario Jreige, Marie Nicod-Lalonde, Marina Silva-Monteiro, John O. Prior, Alban Denys, Adrien Depeursinge, Niklaus Schaefer

Oncotarget, 2017, vol. 9, no. 4, pp. 4549-4558

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Résumé:

Methods: Pretreatment 18F-FDG PET of 47 consecutive patients undergoing 90Y-TARE for uHCC (31 resin spheres, 16 glass spheres) were retrospectively analyzed. For each patient, based on PET radiomics signature from whole-liver semi-automatic segmentation, PFS and OS predictive PET-radiomics scores (pPET-RadScores) were obtained using LASSO Cox regression. Using X-tile software, the optimal score to predict PFS (PFS-pPET-RadScore) and OS (OS-pPET-RadScore) served as cutoff to separate high and low-risk patients. Survival curves were estimated using the Kaplan-Meier method. The prognostic value of PFS and OS-pPET-RadScore, Barcelona-Clinic Liver Cancer staging system and serum alpha-fetoprotein level was analyzed to predict PFS and OS in multivariate analysis.

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging

Article scientifique ArODES

Imon Banerjee, Sadhika Malladi, Daniela Lee, Adrien Depeursinge, Melinda Telli, Jafi Lipson, Daniel Golden, Daniel L. Rubin

Journal of medical imaging, January 2018, vol. 5, no. 1

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Résumé:

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ( ∼96 ) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by ∼13% . Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.

Fundamentals of texture processing for biomedical image analysis :

Chapitre de livre ArODES

a general definition and problem formulation

Adrien Depeursinge, Julien Fageot, Omar Al-Kadi

Biomedical texture analysis : fundamentals, tools and challenges (pp. 7-35). 2017, London : Academic Press

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Résumé:

This chapter aims to provide an overview of the foundations of texture processing for biomedical image analysis. Its purpose is to define precisely what biomedical texture is, how is it different from general texture information considered in computer vision, and what is the general problem formulation to translate 2Dand 3Dtextured patterns from biomedical images to visually and biologically relevant measurements. First, a formal definition of biomedical tex-ture information is proposed from both perceptual and mathematical point of views. Second, a general problem formulation for biomedical texture analysis is introduced, considering that any approach can be characterized as a set of local texture operators and regional aggre-gation functions. The operators allow locally isolating desired texture information in terms of spatial scales and directions of a texture image. The type of desirable operator invariances are discussed, and are found to be different from photographic image analysis. Scalar-valued texture measurements are obtained by aggregating operator’s response maps over regions of interest.

Biomedical texture operators and aggregation functions :

Chapitre de livre ArODES

a methodological review and user's guide

Adrien Depeursinge, Julien Fageot

Biomedical texture analysis : fundamentals, tools and challenges (pp. 55–94). 2017, London : Academic Press

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This chapter reviews most popular texture analysis approaches under novel comparison axes that are specific to biomedical imaging. A concise checklist is proposed as a user guide to assess the relevance of each approach for a particular medical or biological task in hand. We revealed that few approaches are regrouping most of the desirable properties for achieving optimal performance. In particular, moving frames texture representations based on learned steerable operators showed to enable data-specific and rigid-transformation-invariant characterization of local directional patterns, the latter being a fundamental property of biomedical textures. Potential limitations of having recourse to data augmentation and transfer learning for deep convolutional neural networks and dictionary learning approaches to palliate the lack of large annotated training collections in biomedical imaging are mentioned. We conclude by summarizing the strengths and limitations of current approaches, providing insights on key aspects required to build the next generation of biomedical texture analysis approaches.

Web-based tools for exploring the potential of quantitative imaging biomarkers in radiology :

Chapitre de livre ArODES

intensity and texture analysis on the ePAD platform

Roger Schaer, Yashin Dicente Cid, Emel Alkim, Sheryl John, Daniel L. Rubin, Adrien Depeursinge

Biomedical texture analysis : fundamentals, tools and challenges (pp. 379–410). 2017, [S. l.] : Elsevier

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Recent papers in the field of radiomics showed strong evidence that novel image biomarkers based on structural tissue properties have the potential to complement and even surpass invasive and costly biopsy-based molecular assays in certain clinical contexts. To date, very few translations of these research results to clinical practive have been carried out. In addition, a majority of the identified imaging biomarkers are perceived as black boxes by end-users, hindering their acceptance in clinical and research environments. We present a suite of plugins called Quantitative Feature Explore (QFExplore) for the open-access cloud-based ePAD platform enabling the exploration and validation of new imaging biomarkers in a clinical environment. The latter include the extraction, visualization and comparison of intensity- and texture-based quantitative imaging features, regional division of regions of interest to reveal tissue diversity, as well as the construction, use and sharing of user-personalized statistical machine learning models. No software installation is required and the platform can be accessed through any web browser. The relevance of the developed tools is demonstrated in the context of various clinical use-cases. The software is available online.

QuantImage :

Chapitre de livre ArODES

an online tool for high-throughput 3D radiomics feature extraction in PET-CT

Yashin Dicente Cid, Joël Castelli, Roger Schaer, Nathaniel Scher, Anastasia Pomoni, John O. Prior, Adrien Depeursinge

Biomedical texture analysis : fundamentals, tools and challenges (pp. 365-394). 2017, London : Academic Press

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Theprocessesofradiomicsconsistofimage-basedpersonalizedtumorphenotypingforpre-cision medicine. They complement slow, costly and invasive molecular analysis of tumoral tissue. Whereastherelevanceofalargevarietyofquantitativeimagingbiomarkershasbeen demonstrated for various cancer types, most studies were based on 2D image analysis of relatively small patient cohorts. In this work, we propose an online tool for automatically ex-tracting 3D state-of-the-art quantitative imaging features from large batches of patients. The developed platform is called QuantImage and can be accessed from any web browser. Its use is straightforward and can be further parameterized for refined analyses. It relies on a robust 3D processing pipeline allowing normalization across patients and imaging protocols. Theusercansimplydrag-and-dropalargezipfilecontainingallimagedataforabatchofpa-tients and the platform returns a spreadsheet with the set of quantitative features extracted for each patient. It is expected to enable high-throughput reproducible research and the validation of radiomics imaging parameters to shape the future of non-invasive personalized medicine.

Multi-scale and multi-directional biomedical texture analysis :

Chapitre de livre ArODES

finding the needle in the haystack

Adrien Depeursinge

Biomedical texture analysis : fundamentals, tools and challenges (pp. 37-62). 2017, London : Academic Press

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Résumé:

This chapter clarifies the important aspects of biomedical texture analysis under the general framework introduced in Chapter 1. It was proposed that any approach can be character-ized as the combination of local texture operators and regional aggregation functions. The type of scale and directional information that can or cannot be modeled by categories of texture processing methods is revealed through theoretic analyses and experimental valida-tions. Several key aspects are found to be commonly overlooked in the literature and are highlighted. First, we demonstrate the risk of using regions of interest for aggregation that are regrouping tissue types of different natures. Second, a detailed study of the type of directional information important for biomedical texture characterization suggests that fun-damental properties lie in the local organization of image directions. In addition, it was found that most approaches cannot efficiently characterize the latter, and even fewer can do it with invariance to local rotations. We conclude by deriving novel comparison axes to evalu-ate the relevance of biomedical texture analysis methods in a specific medical or biological applicative context.

Biomedical texture analysis :

Livre ArODES

fundamentals, tools and challenges

Adrien Depeursinge, Omar S. Al-Kadi, J.Ross Mitchell

2017, London : Academic Press, 430 p.

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Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images.

Metabolic tumor volume and total lesion glycolysis in oropharyngeal cancer treated with definitive radiotherapy :

Article scientifique ArODES

which treshold is the best predictor of local control ?

Joël Castelli, Adrien Depeursinge, Berardino De Bari, Anne Devilliers, Renaud De Crevoisier, Jean Bourhis, John O. Prior

Clinical nuclear medicine, 2017, vol. 42, no. 6, pp. 281-285

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Text- and content-based medical image retrievals in the VISCERAL retrieval benchmark

Chapitre de livre ArODES

Fan Zhang, Yang Song, Weidong Cai, Adrien Depeursinge, Henning Müller

Cloud-based benchmarking of medical image analysis (pp. 237-249). 2017, Cham : Springer

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Résumé:

Text- and content-based retrieval are the most widely used approaches for medical image retrieval. They capture the similarity between the images from different perspectives: text-based methods rely on manual textual annotations or captions associated with images; content-based approaches are based on the visual content of the images themselves such as colours and textures. Text-based retrieval can better meet the high-level expectations of humans but is limited by the time-consuming annotations. Content-based retrieval can automatically extract the visual features for high-throughput processing; however, its performance is less favourable than the text-based approaches due to the gap between low-level visual features and high-level human expectations. In this chapter, we present the participation from our joint research team of USYD/HES-SO in the VISCERAL retrieval task. Five different methods are introduced, of which two are based on the anatomy–pathology terms, two are based on the visual image content and the last one is based on the fusion of the aforementioned methods. The comparison results, given the different methods indicated that the text-based methods outperformed the content-based retrieval and the fusion of text and visual contents, generated the best performance overall.

A PET-based nomogram for oropharyngeal cancers

Article scientifique ArODES

Joël Castelli, Adrien Depeursinge, V. Ndoh, et al.

European journal of cancer, April 2017, vol. 75, pp. 222-230

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Résumé:

Purpose In the context of locally advanced oropharyngeal cancer (LAOC) treated with definitive radiotherapy (RT) (combined with chemotherapy or cetuximab), the aims of this study were: (1) to identify PET-FDG parameters correlated with overall survival (OS) from a first cohort of patients; then (2) to compute a prognostic score; and (3) finally to validate this scoring system in a second independent cohort of patients. Materials and methods A total of 76 consecutive patients (training cohort from Rennes) treated with chemoradiotherapy or RT with cetuximab for LAOC were used to build a predictive model of locoregional control (LRC) and OS based on PET-FDG parameters. After internal calibration and validation of this model, a nomogram and a scoring system were developed and tested in a validation cohort of 46 consecutive patients treated with definitive RT for LAOC in Lausanne. Results In multivariate analysis, the metabolic tumour volume (MTV) of the primary tumour and the lymph nodes were independent predictive factors for LRC and OS. Internal calibration showed a very good adjustment between the predicted OS and the observed OS at 24 months. Using the predictive score, two risk groups were identified (median OS 42 versus 14 months, p < 0.001) and confirmed in the validation cohort from Lausanne (median OS not reached versus 26 months, p = 0.008). Conclusions This is the first report of a PET-based nomogram in oropharyngeal cancer. Interestingly, it appeared stronger than the classical prognostic factors and was validated in independent cohorts markedly diverging in many aspects, which suggest that the observed signal was robust.

3D solid texture classification using locally-oriented wavelet transforms

Article scientifique ArODES

Yashin Dicente Cid, Henning Müller, Alexandra Platon, Pierre–Alexandre Poletti, Adrien Depeursinge

IEEE transactions on image processing, 2017, vol. 26, no. 4, pp. 1899-1910

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Résumé:

Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3D content. One of the most common methods to analyze 3D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3D. Current state-of-the-art techniques face many challenges when working with 3D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3D Riesz-wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3D Riesz-wavelet transforms. The estimations of local texture orientations are based on higher-order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods achieved an accuracy of 0.95 in the rotated data, three times more than the unaligned Riesz descriptor that achieved 0.32. The accuracy obtained is better than all other techniques that are published and tested on the same database.

Steerable wavelet machines (SWM) :

Article scientifique ArODES

learning moving frames

Adrien Depeursinge, Zsuzsanna Püspöki, John Paul Ward, Michael Unser

IEEE transactions on image processing, April 2017, vol. 26, issue 4, pp. 1626-1636

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Résumé:

We present texture operators encoding class-specific local organizations of image directions (LOIDs) in a rotation-invariant fashion. The LOIDs are key for visual understanding, and are at the origin of the success of the popular approaches, such as local binary patterns (LBPs) and the scale-invariant feature transform (SIFT). Whereas, LBPs and SIFT yield hand-crafted image representations, we propose to learn data-specific representations of the LOIDs in a rotation-invariant fashion. The image operators are based on steerable circular harmonic wavelets (CHWs), offering a rich and yet compact initial representation for characterizing natural textures. The joint location and orientation required to encode the LOIDs is preserved by using moving frames (MFs) texture representations built from locally-steered image gradients that are invariant to rigid motions. In a second step, we use support vector machines to learn a multi-class shaping matrix for the initial CHW representation, yielding data-driven MFs called steerable wavelet machines (SWMs). The SWM forward function is composed of linear operations (i.e., convolution and weighted combinations) interleaved with non-linear steermax operations. We experimentally demonstrate the effectiveness of the proposed operators for classifying natural textures. Our scheme outperforms recent approaches on several test suites of the Outex and the CUReT databases.

2016

A 3-D Riesz-covariance texture model for prediction of nodule recurrence in Lung CT

Article scientifique ArODES

Henning Müller, Adrien Depeursinge, et al.

IEEE Transactions on medical imaging, 2016, vol. 35, issue 12, pp. 2620 - 2630

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This paper proposes a novel imaging biomarker of lung cancer relapse from 3–D texture analysis of CT images. Three–dimensional morphological nodular tissue properties are described in terms of 3–D Riesz–wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra– and inter– variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co–variations between features. The obtained Riesz–covariance descriptors lie on a manifold governed by Riemannian geometry allowing geodesic measurements and differentiations. The latter property is incorporated both into a kernel for support vector machines (SVM) and a manifold–aware sparse regularized classifier. The effectiveness of the presented models is evaluated on a dataset of 110 patients with non–small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy of 81.3–82.7%. The anatomical location of recurrence could be discriminated between local, regional and distant failure with an accuracy of 78.3–93.3%. The obtained results open novel research perspectives by revealing the importance of the nodular regions used to build the predictive models.

Optimized distributed hyperparameter search and simulation for Lung texture classification in CT Usi

Article scientifique ArODES

Roger Schaer, Henning Müller, Adrien Depeursinge

Journal of imaging, 2016, vol. 2, no. 2

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Résumé:

Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no exception. Via a learning framework, very good classification accuracy can be obtained, but several parameters need to be optimized. This article describes a practical framework for efficient distributed parameter optimization. The proposed solutions are applicable for many research groups with heterogeneous computing infrastructures and for various machine learning algorithms. These infrastructures can easily be connected via distributed computation frameworks. We use the Hadoop framework to run and distribute both grid and random search strategies for hyperparameter optimization and cross-validations on a cluster of 21 nodes composed of desktop computers and servers. We show that significant speedups of up to 364 compared to a serial execution can be achieved using our in-house Hadoop cluster by distributing the computation and automatically pruning the search space while still identifying the best-performing parameter combinations. To the best of our knowledge, this is the first article presenting practical results in detail for complex data analysis tasks on such a heterogeneous infrastructure together with a linked simulation framework that allows for computing resource planning. The results are directly applicable in many scenarios and allow implementing an efficient and effective strategy for medical (image) data analysis and related learning approaches.

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Article scientifique ArODES

Jocelyn Barker, Assaf Hoogi, Adrien Depeursinge, Daniel L. Rubin

Medical image analysis, May 2016, vol.30, pp.60–71

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Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1 % (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

2015

Using MapReduce for large-scale medical image analysis

Article scientifique ArODES

Dimitrios Markonis, Roger Schaer, Ivan Eggel, Henning Müller, Adrien Depeursinge

ArXiv,

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The growth of the amount of medical image data produced on a daily basis in modern hospitals forces the adaptation of traditional medical image analysis and indexing approaches towards scalable solutions. The number of images and their dimensionality increased dramatically during the past 20 years. We propose solutions for large-scale medical image analysis based on parallel computing and algorithm optimization. The MapReduce framework is used to speed up and make possible three large-scale medical image processing use-cases: (i) parameter optimization for lung texture segmentation using support vector machines, (ii) content-based medical image indexing, and (iii) three-dimensional directional wavelet analysis for solid texture classification. A cluster of heterogeneous computing nodes was set up in our institution using Hadoop allowing for a maximum of 42 concurrent map tasks. The majority of the machines used are desktop computers that are also used for regular office work. The cluster showed to be minimally invasive and stable. The runtimes of each of the three use-case have been significantly reduced when compared to a sequential execution. Hadoop provides an easy-to-employ framework for data analysis tasks that scales well for many tasks but requires optimization for specific tasks.

Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution computed tomography

Article scientifique ArODES

Adrien Depeursinge, Anne C. Chin, Ann N. Leung, Donato Terrone, Michael Bristow, Glenn Rosen, Daniel L. Rubin

Investigative Radiology, avril 2015, vol. 50, no. 4, pp. 261–267

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Objectives:We propose a novel computational approach for the automated classification of classic versus atypical usual interstitial pneumonia (UIP). Materials and Methods: Thirty-three patients with UIP were enrolled in this study. They were classified as classic versus atypical UIP by a consensus of 2 thoracic radiologists with more than 15 years of experience using the American Thoracic Society evidence–based guidelines for computed tomography diagnosis of UIP. Two cardiothoracic fellows with 1 year of subspecialty training provided independent readings. The system is based on regional characterization of the morphological tissue properties of lung using volumetric texture analysis of multiple-detector computed tomography images. A simple digital atlas with 36 lung subregions is used to locate texture properties, from which the responses of multidirectional Riesz wavelets are obtained. Machine learning is used to aggregate and to map the regional texture attributes to a simple score that can be used to stratify patients with UIP into classic and atypical subtypes. Results: We compared the predictions on the basis of regional volumetric texture analysis with the ground truth established by expert consensus. The area under the receiver operating characteristic curve of the proposed score was estimated to be 0.81 using a leave-one-patient-out cross-validation, with high specificity for classic UIP. The performance of our automated method was found to be similar to that of the 2 fellows and to the agreement between experienced chest radiologists reported in the literature. However, the errors of our method and the fellows occurred on different cases, which suggests that combining human and computerized evaluations may be synergistic. Conclusions: Our results are encouraging and suggest that an automated system may be useful in routine clinical practice as a diagnostic aid for identifying patients with complex lung disease such as classic UIP, obviating the need for invasive surgical lung biopsy and its associated risks.

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

Article scientifique ArODES

Adrien Depeursinge, Masahiro Yanagawa, Ann N. Leung, Daniel L. Rubin

Medical Physics, April 2015, vol. 42, no. 4, pp. 2054-2063

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Purpose: To investigate the importance of presurgical computed tomography (CT) intensity and texture information from ground-glass opacities (GGO) and solid nodule components for the prediction of adenocarcinoma recurrence. Methods: For this study, 101 patients with surgically resected stage I adenocarcinoma were selected. During the follow-up period, 17 patients had disease recurrence with six associated cancer-related deaths. GGO and solid tumor components were delineated on presurgical CT scans by a radiologist. Computational texture models of GGO and solid regions were built using linear combinations of steerable Riesz wavelets learned with linear support vector machines (SVMs). Unlike other traditional texture attributes, the proposed texture models are designed to encode local image scales and directions that are specific to GGO and solid tissue. The responses of the locally steered models were used as texture attributes and compared to the responses of unaligned Riesz wavelets. The texture attributes were combined with CT intensities to predict tumor recurrence and patient hazard according to disease-free survival (DFS) time. Two families of predictive models were compared: LASSO and SVMs, and their survival counterparts: Cox-LASSO and survival SVMs. Results: The best-performing predictive model of patient hazard was associated with a concordance index (C-index) of 0.81±0.02 and was based on the combination of the steered models and CT intensities with survival SVMs. The same feature group and the LASSO model yielded the highest area under the receiver operating characteristic curve (AUC) of 0.8±0.01 for predicting tumor recurrence, although no statistically significant difference was found when compared to using intensity features solely. For all models, the performance was found to be significantly higher when image attributes were based on the solid components solely versus using the entire tumors (p < 3.08×10−5). Conclusions: This study constitutes a novel perspective on how to interpret imaging information from CT examinations by suggesting that most of the information related to adenocarcinoma aggressiveness is related to the intensity and morphological properties of solid components of the tumor. The prediction of adenocarcinoma relapse was found to have low specificity but very high sensitivity. Our results could be useful in clinical practice to identify patients for which no recurrence is expected with a very high confidence using a presurgical CT scan only. It also provided an accurate estimation of the risk of recurrence after a given duration t from surgical resection (i.e., C-index = 0.81±0.02).

2024

Interpretability of uncertainty :

Conférence ArODES

exploring cortical lesion segmentation in multiple sclerosis

Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza, Mario Ocampo-Pineda, Po-Jui Lu, Matthias Weigel, Xinjie Chen, Adrien Depeursinge, Cristina Granziera, Henning Müller, Meritxell Bach Cuadra

Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops

PEOPLE@HES-SO Annuaire et Répertoire des compétences

Depeursinge Adrien

Professeur-e HES Ordinaire

Professeur-e HES Ordinaire

Research Interests

PEOPLE@HES-SO
Annuaire et Répertoire des compétences