Affiliation:
1. Faculty of Information Technology Monash University Clayton Australia
2. Department of Applied Data Science Noroff University College Kristiansand Norway
3. School of Information Technology Monash University Kuala Lumpur Malaysia
Abstract
AbstractEarly diagnosis of multiple sclerosis (MS) through the delineation of lesions in the brain magnetic resonance imaging is important in preventing the deteriorating condition of MS. This study aims to develop a modified U‐Net model for automating lesions segmentation in MS more accurately. The proposed modified U‐Net uses residual dense blocks to replace the standard convolutional stacks and incorporates three axes (axial, sagittal, and coronal) of 2D slice images as input. Furthermore, a custom fusion method is also introduced for merging the predicted lesions from different axes. The model was implemented on ISBI2015 and OpenMS data sets. On ISBI2015, the proposed model achieves the best overall score of 93.090% and DSC of 0.857 on the OpenMS data set.
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials
Reference63 articles.
1. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition
2. The importance of early diagnosis of multiple sclerosis;Miller JR;J Manag Care Pharm,2004
3. Improving outcomes in multiple sclerosis through early diagnosis and effective management;Waubant E;Prim Care Companion CNS Disord,2012
4. Multiple sclerosis
5. Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network