カミヤ　コウヘイ   Kamiya Kohei   神谷　昂平    所属   東邦大学  医学部 医学科（大森病院）    職種   講師
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.
掲載誌名	正式名：NeuroImage ISSNコード：10538119
出版社	ACADEMIC PRESS INC ELSEVIER SCIENCE
巻・号・頁	184,pp.901-915
著者・共著者	Charley Gros,Benjamin De Leener,Atef Badji,Josefina Maranzano,Dominique Eden,Sara M Dupont,Jason Talbott,Ren Zhuoquiong,Yaou Liu,Tobias Granberg,Russell Ouellette,Yasuhiko Tachibana,Masaaki Hori,Kouhei Kamiya,Lydia Chougar,Leszek Stawiarz,Jan Hillert,Elise Bannier,Anne Kerbrat,Gilles Edan,Pierre Labauge,Virginie Callot,Jean Pelletier,Bertrand Audoin,Henitsoa Rasoanandrianina,Jean-Christophe Brisset,Paola Valsasina,Maria A Rocca,Massimo Filippi,Rohit Bakshi,Shahamat Tauhid,Ferran Prados,Marios Yiannakas,Hugh Kearney,Olga Ciccarelli,Seth Smith,Constantina Andrada Treaba,Caterina Mainero,Jennifer Lefeuvre,Daniel S Reich,Govind Nair,Vincent Auclair,Donald G McLaren,Allan R Martin,Michael G Fehlings,Shahabeddin Vahdat,Ali Khatibi,Julien Doyon,Timothy Shepherd,Erik Charlson,Sridar Narayanan,Julien Cohen-Adad
発行年月	2019/01
概要	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 194
DOI	10.1016/j.neuroimage.2018.09.081
PMID	30300751