Multiple Sclerosis Lesion Segmentation -- A Survey of Supervised CNN-Based Methods
This paper provides a survey of existing methods for researchers and practitioners working on MS lesion segmentation, an incremental contribution to the field.
This survey investigates supervised Convolutional Neural Network (CNN)-based methods for Multiple Sclerosis (MS) lesion segmentation, a critical task for quantitative MRI analysis. It decouples reviewed works into algorithmic components and compares results for methods evaluated on public benchmark datasets.
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.