{"version":1,"kind":"Article","sha256":"","slug":"613","location":"","dependencies":[],"doi":"10.54294/6eyg0w","frontmatter":{"title":"An Automatic Segmentation of T2-FLAIR Multiple Sclerosis Lesions","abstract":"Multiple sclerosis diagnosis and patient follow-up can be helped by an evaluation of the lesion load in MRI sequences. A lot of automatic methods to segment these lesions are available in the literature. The MICCAI workshop Multiple Sclerosis (MS) lesion segmentation Challenge 08 allows to test and compare these algorithms. This paper presents a method designed to detect hyperintense signal area on T2-FLAIR sequence and its results on the Challenge test data. The proposed algorithm uses only three conventional MRI sequences: T1, T2 and T2-FLAIR. First, images are cropped, spatially unbiased and skull-stripped. A segmentation of the brain into its different compartments is performed on the T1 and the T2 sequences. From these segmentations, a threshold for the T2-FLAIR sequence is automatically computed. Then postprocessing operations select the most plausible lesions in the obtained hyperintense signals. Global result on the test data (80/100) is close to the inter-expert variability (90/100).","license":"You are licensing your work to Kitware Inc. under the\nCreative Commons Attribution License Version 3.0.\n\nKitware Inc. agrees to the following:\n\nKitware is free\n * to copy, distribute, display, and perform the work\n * to make derivative works\n * to make commercial use of the work\n\nUnder the following conditions:\n\\\"by Attribution\\\" - Kitware must attribute the work in the manner specified by the author or licensor.\n\n * For any reuse or distribution, they must make clear to others the license terms of this work.\n * Any of these conditions can be waived if they get permission from the copyright holder.\n\nYour fair use and other rights are in no way affected by the above.\n\nThis is a human-readable summary of the Legal Code (the full license) available at\nhttp://creativecommons.org/licenses/by/3.0/legalcode","keywords":["Multiple sclerosis","segmentation"],"authors":[{"name":"Souplet, Jean-Christophe","email":"jean-christophe.souplet@sophia.inria.fr","affiliations":["INRIA"],"corresponding":true},{"name":"Lebrun, Christine","affiliations":[]},{"name":"Ayache, Nicholas","affiliations":[]},{"name":"Malandain, Gregoire","affiliations":[]}],"date_submitted":"2008-07-15T13:51:28Z","external_publication_id":613,"revision_cids":["bafkreiath3s2gswnybj5yunudjq7cqkkfq53hlsbtauqftfhemystophhe"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreidodhwpygljooxm274ukdipzmpgbp645vgmtedgcojzavf6qnkd6q","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":12337,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreihixyhgeo3olkpkfk5nrq7rrjcui3bdbkjflmwjjlsajwhcc2vp34","title":"root/reviews.md","filename":"reviews.md","extra":{"size_bytes":1612,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreif6r4ccwp7mo2su3hnscdnly5ihu6fln3od7sojkjjypkp2bmifzm","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":895791,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7","ref8","ref9","ref10","ref11","ref12","ref13","ref14","ref15","ref16","ref17","ref18","ref19","ref20","ref21","ref22","ref23"]},"data":{"ref1":{"label":"ref1","enumerator":"1","html":"(document)+Brain+120+2059+69+November 1997+F Barkhof+M Filippi+D H Miller+P Scheltens+C H Polman+G Comi+H J Ader+N Losseff+J Valk"},"ref2":{"label":"ref2","enumerator":"2","url":"https://doi.org/10.1016/j.ijrobp.2004.08.055","html":"Atlas-based automatic segmentation of MR images: validation study on the brainstem in radiotherapy context+Int J Radiat Oncol Biol Phys+1+1+3+98+January 2005+Bondiau Pierre-Yves+Marcy Pierre-Yves+Habrand Jean-Louis"},"ref3":{"label":"ref3","enumerator":"3","url":"https://doi.org/10.1016/j.media.2008.01.002","html":"An efficient locally affine framework for the smooth registration of anatomical structures+Medical Image Analysis+1+4+3+441+2008+Olivier Commowick+Bondiau Pierre-Yves"},"ref4":{"label":"ref4","enumerator":"4","url":"https://doi.org/10.1111/j.2517-6161.1977.tb01600.x","html":"Maximum likelihood for incomplete data via the EM algorithm+Journal of the Royal Statistical Society+1+1+2+38+1977+A. 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