Carole H. Sudre

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14ranked-venue papers

5as first author

5since 2021 · last 2024

0000-0001-5753-428XORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 12 · 5 first-author · 4 since 2021Graphics, computer vision, multimedia, augmented reality and games · 6 · 2 first-author · 2 since 2021Artificial intelligence and machine learning · 2 · 1 since 2021

Year	Publication	Venue	Position
2024	Confidence Intervals Uncovered: Are We Ready for Real-World Medical Imaging AI? Evangelia Christodoulou, Annika Reinke, Rola Houhou, Piotr Kalinowski, Selen Erkan, Carole H. Sudre, Ninon Burgos, Sofiène Boutaj, Sophie Loizillon, Maëlys Solal, Nicola Rieke, Veronika Cheplygina, Michela Antonelli, Leon D. Mayer, Minu Tizabi, Manuel Jorge Cardoso, Amber L. Simpson, Paul F. Jaeger, Annette Kopp-Schneider, Gaël Varoquaux, Olivier Colliot, Lena Maier-Hein	MICCAI (10)	6
2024	Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021 Carole H. Sudre, Kimberlin M. H. van Wijnen, Florian Dubost, Hieab Adams, David Atkinson, Frederik Barkhof, Mahlet A. Birhanu, Esther Bron, Robin Camarasa, Nish Chaturvedi, Qi Dou 0001, Tavia E. Evans, Ivan Ezhov, Haojun Gao, Marta Gironés-Sangüesa, Juan Domingo Gispert, Beatriz Gomez Anson, Alun D. Hughes, Mohammad Arfan Ikram, Silvia Ingala, Hans Rolf Jäger, Florian Kofler, Hugo J. Kuijf, Denis Kutnar, Bo Li 0088, Luigi Lorenzini, Bjoern Menze, José Luis Molinuevo, Yiwei Pan, Élodie Puybareau, Rafael Rehwald, Ruisheng Su, Lorna Smith, Therese Tillin, Guillaume Tochon, Hélène Urien, Bas H. M. van der Velden, Isabelle F. van der Velpen, Benedikt Wiestler, Frank J. Wolters, Pinar Yilmaz, Marius de Groot, Meike W. Vernooij, Marleen de Bruijne	Medical Image Anal.	1
2023	Why is the Winner the Best? abstract International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein	CVPR	54
2023	ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans abstract An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM. Cher Bass, Mariana da Silva, Carole H. Sudre, Logan Z. J. Williams, Helena S. Sousa, Petru-Daniel Tudosiu, Fidel Alfaro-Almagro, Sean P. Fitzgibbon, Matthew F. Glasser, Stephen M. Smith 0001, Emma C. Robinson	IEEE Trans. Medical Imaging	3
2021	Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets abstract Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on task-specific imaging protocols including a task-specific set of imaging modalities. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from aggregated task-specific hetero-modal domain-shifted and partially-annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper bound of the risk to deal with heterogeneous imaging modalities across datasets. To deal with potential domain shift, we integrated and tested three conventional techniques based on data augmentation, adversarial learning and pseudo-healthy generation. For each individual task, our joint approach reaches comparable performance to task-specific and fully-supervised models. The proposed framework is assessed on two different types of brain lesions: White matter lesions and gliomas. In the latter case, lacking a joint ground-truth for quantitative assessment purposes, we propose and use a novel clinically-relevant qualitative assessment methodology. Reuben Dorent, Thomas C. Booth, Wenqi Li 0001, Carole H. Sudre, Sina Kafiabadi, Manuel Jorge Cardoso, Sébastien Ourselin, Tom Vercauteren	Medical Image Anal.	4
2020	Test-Time Unsupervised Domain Adaptation Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre, Mark S. Graham, Parashkev Nachev, Manuel Jorge Cardoso	MICCAI (1)	3
2020	ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping abstract Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present a novel framework for creating class specific FA maps through image-to-image translation. We propose the use of a VAE-GAN to explicitly disentangle class relevance from background features for improved interpretability properties, which results in meaningful FA maps. We validate our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing (UK Biobank), and (simulated) lesion detection. We show that FA maps generated by our method outperform baseline FA methods when validated against ground truth. More significantly, our approach is the first to use latent space sampling to support exploration of phenotype variation. Cher Bass, Mariana da Silva, Carole H. Sudre, Petru-Daniel Tudosiu, Stephen M. Smith 0001, Emma C. Robinson	NeurIPS	3
2020	A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal abstract Patient movement during the acquisition of magnetic resonance images (MRI) can cause unwanted image artefacts. These artefacts may affect the quality of clinical diagnosis and cause errors in automated image analysis. In this work, we present a method for generating realistic motion artefacts from artefact-free magnitude MRI data to be used in deep learning frameworks, increasing training appearance variability and ultimately making machine learning algorithms such as convolutional neural networks (CNNs) more robust to the presence of motion artefacts. By modelling patient movement as a sequence of randomly-generated, 'demeaned', rigid 3D affine transforms, we resample artefact-free volumes and combine these in k-space to generate motion artefact data. We show that by augmenting the training of semantic segmentation CNNs with artefacts, we can train models that generalise better and perform more reliably in the presence of artefact data, with negligible cost to their performance on clean data. We show that the performance of models trained using artefact data on segmentation tasks on real-world test-retest image pairs is more robust. We also demonstrate that our augmentation model can be used to learn to retrospectively remove certain types of motion artefacts from real MRI scans. Finally, we show that measures of uncertainty obtained from motion augmented CNN models reflect the presence of artefacts and can thus provide relevant information to ensure the safe usage of deep learning extracted biomarkers in a clinical pipeline. Richard Shaw, Carole H. Sudre, Thomas Varsavsky, Sébastien Ourselin, Manuel Jorge Cardoso	IEEE Trans. Medical Imaging	2
2019	Let's Agree to Disagree: Learning Highly Debatable Multirater Labelling Carole H. Sudre, Beatriz Gomez Anson, Silvia Ingala, Chris D. Lane, Daniel Jimenez, Lukas Haider, Thomas Varsavsky, Ryutaro Tanno, Lorna Smith, Sébastien Ourselin, Hans Rolf Jäger, Manuel Jorge Cardoso	MICCAI (4)	1
2019	Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge abstract Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation. Hugo J. Kuijf, Adrià Casamitjana, D. Louis Collins, Mahsa Dadar, Achilleas Georgiou, Mohsen Ghafoorian, Dakai Jin, April Khademi, Jesse Knight, Hongwei Li 0004, Xavier Lladó, J. Matthijs Biesbroek, Miguel Luna, Qaiser Mahmood, Richard McKinley, Alireza Mehrtash, Sébastien Ourselin, Bo-yong Park, Hyunjin Park, Simon Pezold, Élodie Puybareau, Jeroen de Bresser, Letícia Rittner, Carole H. Sudre, Sergi Valverde, Verónica Vilaplana, Roland Wiest, Yongchao Xu, Ziyue Xu 0004, Guodong Zeng, Jianguo Zhang 0001, Guoyan Zheng, Rutger Heinen, Christopher Li Hsian Chen, Wiesje M. van der Flier, Frederik Barkhof, Max A. Viergever, Geert Jan Biessels, Simon Andermatt, Mariana P. Bento, Matt Berseth, Mikhail Belyaev, Manuel Jorge Cardoso	IEEE Trans. Medical Imaging	25
2018	Thalamic Nuclei Segmentation Using Tractography, Population-Specific Priors and Local Fibre Orientation Carla Semedo, Manuel Jorge Cardoso, Sjoerd B. Vos, Carole H. Sudre, Martina Bocchetta, Annemie Ribbens, Dirk Smeets, Jonathan D. Rohrer, Sébastien Ourselin	MICCAI (3)	4
2017	Longitudinal segmentation of age-related white matter hyperintensities abstract Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns. Carole H. Sudre, Manuel Jorge Cardoso, Sébastien Ourselin	Medical Image Anal.	1
2015	Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation abstract In neuroimaging studies, pathologies can present themselves as abnormal intensity patterns. Thus, solutions for detecting abnormal intensities are currently under investigation. As each patient is unique, an unbiased and biologically plausible model of pathological data would have to be able to adapt to the subject's individual presentation. Such a model would provide the means for a better understanding of the underlying biological processes and improve one's ability to define pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a hierarchical fully unsupervised model selection framework for neuroimaging data which enables the distinction between different types of abnormal image patterns without pathological a priori knowledge. Its application on simulated and clinical data demonstrated the ability to detect abnormal intensity clusters, resulting in a competitive to improved behavior in white matter lesion segmentation when compared to three other freely-available automated methods. Carole H. Sudre, Manuel Jorge Cardoso, Willem H. Bouvy, Geert Jan Biessels, Josephine Barnes, Sébastien Ourselin	IEEE Trans. Medical Imaging	1
2014	Bayesian Model Selection for Pathological Data Carole H. Sudre, Manuel Jorge Cardoso, Willem H. Bouvy, Geert Jan Biessels, Josephine Barnes, Sébastien Ourselin	MICCAI (1)	1