Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Commowick, Olivier; Istace, Audrey; Kain, Michaël; Laurent, Baptiste; Leray, Florent; Simon, M; Camarasu-Pop, Sorina; Girard, Pascal; Améli, Roxana; Ferré, J.-C.; Kerbrat, Anne; Tourdias, Thomas; Cervenansky, Frédéric; Glatard, Tristan; Beaumont, J.; Doyle, Senan; Forbes, Florence; Knight, Jesse; Khademi, April; Mahbod, Amirreza; Wang, Chunliang; McKinley, Richard; Wagner, Franca; Muschelli, John; Sweeney, Elizabeth; Roura, Eloy; Lladó, Xavier; Santos, Michel M.; Santos, Wellington Pinheiro dos; Silva-Filho, Abel G.; Tomas-Fernandez, Xavier; Urien, Hélène; Bloch, Isabelle; Valverde, Sergi; Cabezas, Mariano; Vera-Olmos, Francisco Javier; Malpica, Norberto; Guttmann, Charles R.G.; Vukusic, Sandra; Edan, Gilles; Dojat, Michel; Styner, Martin; Warfield, Simon K.; Cotton, François; Barillot, Christian

doi:10.1038/s41598-018-31911-7

Cited by 198 publications

(195 citation statements)

References 34 publications

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“…size of a region and set of thresholds, that must be accurately tuned on a set of cases. Evaluation was performed on the MICCAI 2016 MS lesions segmentation challenge dataset, comprising clinical images acquired with different MR scanners and acquisition protocols [9]. This is an important aspect when developing techniques that are meant to be employed in the clinical practice.…”

Section: Resultsmentioning

confidence: 99%

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Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRI

Galassi

Commowick

Vallée

et al. 2019

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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We introduce a new framework for the automated and unsupervised segmentation of Multiple Sclerosis lesions from multimodal Magnetic Resonance images. It relies on a voxel-wise approach to detect local white matter abnormalities, with an a-contrario analysis, which takes into account local information. First, a voxel-wise comparison of multimodal patient images to a set of controls is performed. Then, regionbased probabilities are estimated using an a-contrario approach. Finally, correction for multiple testing is performed. Validation was undertaken on a multi-site clinical dataset of 53 MS patients with various number and volume of lesions. We showed that the proposed framework outperforms the widely used FDR-correction for this type of analysis, particularly for low lesion loads.

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Section: Resultsmentioning

confidence: 99%

“…MS patients. We evaluated the proposed method on the MICCAI 2016 MS lesion segmentation challenge dataset [9]. It included 53 images of patients suffering from MS (15 training images and 38 testing images; evaluation on the testing images can be performed by submission to the evaluation platform 1 ).…”

Section: Dataset and Pre-processingmentioning

confidence: 99%

Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRI

Galassi

Commowick

Vallée

et al. 2019

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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“…The data transfer module (DTM) is used to exchange data between the image archive and the computing platforms. The architecture was successfully used for hosting two MICCAI Human imaging challenges on Multiple Sclerosis (Commowick et al, 2018) and PET tumor segmentation (Hatt et al, 2018).…”

Section: Architecture Overviewmentioning

confidence: 99%

Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies

Kain

Bodin

Loury

et al. 2020

Front. Neuroinform.

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Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization of acquisition and postprocessing techniques. Clearly, there is a need of appropriate tools for the management and sharing of data, post-processing and analysis methods dedicated to small animal imaging. Solutions developed for Human imaging studies cannot be directly applied to this specific domain. In this paper, we present the Small Animal Shanoir (SAS) solution for supporting animal population imaging using tools compatible with open data. The integration of automated workflow tools ensures accessibility and reproducibility of research outputs. By sharing data and imaging processing tools, hosted by SAS, we promote data preparation and tools for reproducibility and reuse, and participation in multicenter or replication "open science" studies contributing to the improvement of quality science in preclinical domain. SAS is a first step for promoting open science for small animal imaging and a contribution to the valorization of data and pipelines of reference.

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“…Accurate identification of MS lesions in MRI images is extremely difficult due to variability in lesion location, size, and shape, in addition to anatomical variability across patients. Since manual segmentation requires expert knowledge, it is time consuming and prone to intra-and inter-expert variability, several methods have been proposed to automatically segment MS lesions (García-Lorenzo et al, 2013;Commowick et al, 2018;Galassi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data

Ackaouy

Courty

Vallée

et al. 2020

Front. Comput. Neurosci.

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Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to a reproducibility issue across different image domains. MS images can have highly variable characteristics across patients, MRI scanners and imaging protocols; retraining a supervised model with data from each new domain is not a feasible solution because it requires manual annotation from expert radiologists. In this work, we explore an unsupervised solution to the problem of domain shift. We present a framework, Seg-JDOT, which adapts a deep model so that samples from a source domain and samples from a target domain sharing similar representations will be similarly segmented. We evaluated the framework on a multi-site dataset, MICCAI 2016, and showed that the adaptation toward a target site can bring remarkable improvements in a model performance over standard training.

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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Cited by 198 publications

References 34 publications

Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRI

Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRI

Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies

Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data

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