@inproceedings{2d80d7139c574fbbaa7d2af8080d7fc0,
title = "Multiple Sclerosis Brain Lesion Segmentation with Different Architecture Ensembles",
abstract = "White matter lesion (WML) segmentation applied to magnetic resonance imaging (MRI) scans of people with multiple sclerosis has been an area of extensive research in recent years. As with most tasks in medical imaging, deep learning (DL) methods have proven very effective and have quickly replaced existing methods. Despite the improvement offered by these networks, there are still shortcomings with these DL approaches. In this work, we compare several DL algorithms, as well as methods for ensembling the results of those algorithms, for performing MS lesion segmentation. An ensemble approach is shown to best estimate total WML and has the highest agreement with manual delineations.",
keywords = "deep learning, ensemble, MRI, multiple sclerosis, neuroimaging, segmentation, white matter lesions",
author = "Pouria Tohidi and Remedios, {Samuel W.} and Greenman, {Danielle L.} and Muhan Shao and Shuo Han and Dewey, {Blake E.} and Reinhold, {Jacob C.} and Chou, {Yi Yu} and Pham, {Dzung L.} and Prince, {Jerry L.} and Aaron Carass",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE; Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2623302",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gimi, {Barjor S.} and Andrzej Krol",
booktitle = "Medical Imaging 2022",
}