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Automatic multiple sclerosis lesion segmentation using hybrid artificial neural networks
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0002-0442-3524
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0002-7750-1917
2016 (English)In: MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure, p. 29-36Article in journal (Refereed) Published
Abstract [en]

Multiple sclerosis (MS) is a demyelinating disease which could cause severe motor and cognitive deterioration. Segmenting MS lesions could be highly beneficial for diagnosing, analyzing and monitoring treatment efficacy. To do so, manual segmentation, performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. The aim of this work was to propose an automatic method for MS lesion segmentation and evaluate it using brain images available within the MICCAI MS segmentation challenge. The proposed method employs supervised artificial neural network based algorithm, exploiting intensity-based and spatial-based features as the input of the network. This method achieved relatively accurate results with acceptable training and testing time for training datasets.

Place, publisher, year, edition, pages
2016. p. 29-36
Keywords [en]
Multiple sclerosis segmentation, artificial neural networks, machine learning, MRI
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-258881OAI: oai:DiVA.org:kth-258881DiVA, id: diva2:1350238
Note

QC 20191025

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-10-25Bibliographically approved

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CiteExportLink to record
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