{"version":1,"kind":"Article","sha256":"","slug":"610","location":"","dependencies":[],"doi":"10.54294/ksvxf6","frontmatter":{"title":"Automated MS-Lesion Segmentation by K-Nearest Neighbor Classification","abstract":"This paper proposes a new method for fully automated multiple sclerosis (MS) lesion segmentation in cranial magnetic resonance (MR) imaging. The algorithm uses the T1-weighted and the fluid attenuation inversion recovery scans. It is based the K-Nearest Neighbor (KNN) classification technique. The data has been acquired at the Children�s Hospital Boston (CHB) and the University of North Carolina (UNC). Manual segmentations, composed by a human expert of the CHB, were used for training of the KNN-classification. The method uses voxel location and signal intensity information for determination of the probability being a lesion per voxel, thus generating probabilistic segmentation images. By applying a threshold on the probabilistic images binary segmentations are derived. Automatic segmentations were performed on a set of testing images, and compared with manual segmentations from a CHB and a UNC expert rater. Furthermore, a combined segmentation was composed from segmentations from different algorithms, and used for evaluation. The proposed method shows good resemblance with the segmentations of the CHB rater. High specificity and lower specificity has been observed in comparison with the combined segmentations. Over- and undersegmentation can be easily corrected in this procedure by varying the threshold on the probabilistic segmentation image. The proposed method offers an automated and fully reproducible approach that accurate and applicable on standard clinical MR images.","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":["lesion","MS","Multiple Sclerosis","brain","segmentation","white matter"],"authors":[{"name":"Anbeek, Petronella","email":"p.anbeek@umcutrecht.nl","affiliations":["Image Sciences Institute, University Medical Center Utrecht"],"corresponding":true},{"name":"Vincken, Koen L.","affiliations":[]},{"name":"Viergever, Max A.","affiliations":[]}],"date_submitted":"2008-07-14T21:00:48Z","external_publication_id":610,"revision_cids":["bafkreifyr6iqgpxeigryrtsdrnvb4xcqry3h3duojkxm47nbayudnb7dre"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreieu4fxgi5iz6llpajsg4scw7ewfvigxwne67i2jklp6p5nrmr5g5i","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":9040,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreigz5q6f7tjj3pbewqr7es5e7i5cfwzqua2th4dvqfq7kdpjfg5fvq","title":"root/reviews.md","filename":"reviews.md","extra":{"size_bytes":2034,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreiesyjhyrxjup73qki53lov3ff2zsr7dskddpryiefbud2re2mbvlu","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":105043,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7","ref8"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.3174/ajnr.a0795","html":"Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis+AJNR Am J Neuroradiol+2008+29+2+340+6+Y. 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