Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Charley Gros 1 Benjamin De Leener 1 Atef Badji Josefina Maranzano Dominique Eden Sara Dupont Jason Talbott Ren Zhuoquiong Yaou Liu Tobias Martin 2 Russell Martin 3 Yasuhiko Tachibana Masaaki Hori Kouhei Kamiya Lydia Chougar 4 Leszek Stawiarz Jan Hillert 5 Elise Bannier 6, 7 Anne Kerbrat 6, 8 Gilles Edan 9, 8 Pierre Labauge 10 Virginie Callot 11 Jean Pelletier 11 Bertrand Audoin 11 Henitsoa Rasoanandrianina Jean-Christophe Brisset 12 Paola Valsasina Maria Rocca Massimo Filippi 13 Rohit Bakshi 14 Shahamat Tauhid Ferran Prados 15 Marios Yiannakas Hugh Kearney Olga Ciccarelli 16 Seth Smith Constantina Andrada Martin Caterina Martin Jennifer Lefeuvre Daniel Reich Govind Nair Vincent Auclair 17 Donald Mclaren 17 Allan Martin Michael Fehlings 18 Shahabeddin Vahdat Ali Khatibi Julien Doyon 19 Timothy Shepherd Erik Charlson Sridar Narayanan Julien Cohen-Adad 1 Tobias Granberg Russell Ouellette Constantina Andrada Treaba Caterina Mainero
Abstract : The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.
Type de document :
Article dans une revue
NeuroImage, Elsevier, 2019, 184, pp.901 - 915. 〈10.1016/j.neuroimage.2018.09.081〉
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Soumis le : lundi 26 novembre 2018 - 09:28:23
Dernière modification le : vendredi 11 janvier 2019 - 16:23:17

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Charley Gros, Benjamin De Leener, Atef Badji, Josefina Maranzano, Dominique Eden, et al.. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. NeuroImage, Elsevier, 2019, 184, pp.901 - 915. 〈10.1016/j.neuroimage.2018.09.081〉. 〈hal-01934566〉



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