Multiple Sclerosis brain lesion segmentation with different architecture ensembles

White matter lesion (WML) segmentation applied to magnetic resonance imaging (MRI) scans of people with multiple sclerosis has been an area of extensive research in recent years. As with most tasks in medical imaging, deep learning (DL) methods have proven very effective and have quickly replaced existing methods. Despite the improvement offered by these networks, there are still shortcomings with these DL approaches. In this work, we compare several DL algorithms, as well as methods for ensembling the results of those algorithms, for performing MS lesion segmentation. An ensemble approach is shown to best estimate total WML and has the highest agreement with manual delineations.

近年来，对具有多发性硬化症患者的磁共振成像（MRI）扫描应用于磁共振成像（MRI）扫描已成为广泛研究的领域。与医学成像中的大多数任务一样，深度学习（DL）方法已被证明非常有效，并迅速取代了现有方法。尽管这些网络提供了改善，但这些DL方法仍然存在缺点。在这项工作中，我们比较了几种DL算法以及结合这些算法的结果的方法，用于执行MS病变分割。显示合奏方法可以最好地估计WML的总估计，并且与手动描述具有最高的一致性。