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Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.
https://repo.qst.go.jp/records/49340
https://repo.qst.go.jp/records/49340360cd7aa-83a7-451d-a348-8f734d44cb1d
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2018-12-19 | |||||
| タイトル | ||||||
| タイトル | Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Gros, Charley
× Gros, Charley× De Leener, Benjamin× Badji, Atef× Maranzano, Josefina× Eden, Dominique× M Dupont, Sara× Talbott, Jason× Zhuoquiong, Ren× Liu, Yaou× Granberg, Tobias× Ouellette, Russell× Tachibana, Yasuhiko× Hori, Masaaki× Kamiya, Kouhei× Chougar, Lydia× Stawiarz, Leszek× Hillert, Jan× Bannier, Elise× Kerbrat, Anne× Edan, Gilles× Labauge, Pierre× Callot, Virginie× Pelletier, Jean× Audoin, Bertrand× Rasoanandrianina, Henitsoa× Jean-Christophe, Brisset× Valsasina, Paola× A Rocca, Maria× Filippi, Massimo× Bakshi, Rohit× Tauhid, Shahamat× Prados, Ferran× Yiannakas, Marios× Kearney, Hugh× Ciccarelli, Olga× Smith, Seth× Andrada Treaba, Constantina× Mainero, Caterina× Lefeuvre, Jennifer× S Reich, Daniel× Nair, Govind× Auclair, Vincent× G McLaren, Donald× R Martin, Allan× G Fehlings, Michael× Vahdat, Shahabeddin× Khatibi, Ali× Doyon, Julien× Shepherd, Timothy× Charlson, Erik× Narayanan, Sridar× Julien, Cohen-Adad× Tachibana, Yasuhiko |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | 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 (T-, T-, and T-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. | |||||
| 書誌情報 |
NeuroImage 巻 184, p. 901-915, 発行日 2018-10 |
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| 出版者 | ||||||
| 出版者 | Elsevier | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1053-8119 | |||||
| PubMed番号 | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 30300751 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1016/j.neuroimage.2018.09.081 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S1053811918319578?via%3Dihub | |||||
