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2021 | OriginalPaper | Buchkapitel

Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods

verfasst von : Huahong Zhang, Ipek Oguz

Erschienen in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Verlag: Springer International Publishing

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Abstract

Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. The recent success of deep learning techniques in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works into their algorithmic components and discuss each separately. For methods that provide evaluations on public benchmark datasets, we report comparisons between their results.

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Vorheriges Kapitel Glioma Diagnosis and Classification: Illuminating the Gold Standard

Nächstes Kapitel Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics

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Titel: Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods
verfasst von: Huahong Zhang
Ipek Oguz
Verlag: Springer International Publishing
Buch: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-030-72083-4

Electronic ISBN: 978-3-030-72084-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-72084-1_2

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