论文标题
多发性硬化病变细分 - 对基于CNN的方法的调查
Multiple Sclerosis Lesion Segmentation -- A Survey of Supervised CNN-Based Methods
论文作者
论文摘要
病变分割是对多发性硬化症患者的MRI扫描进行定量分析的核心任务。深度学习技术在各种医学图像分析应用中的最新成功使社区对这个具有挑战性的问题的兴趣重新引起了人们的兴趣，并导致了新算法开发的一系列活动。在这项调查中，我们研究了基于CNN的MS病变细分方法。我们将这些审查的作品与它们的算法组成部分分离，并分别讨论。对于提供公共基准数据集评估的方法，我们报告了其结果之间的比较。
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.
