论文标题
从纵向数据中的时空学习多发性硬化病变细分
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
论文作者
论文摘要
进行纵向大脑MR扫描中多发性硬化病（MS）病变的分割以监测MS病变的进展。我们假设纵向数据中的时空提示可以帮助分割算法。因此，我们通过定义两个时间点之间的可变形注册的辅助自我监督任务来提出一种多任务学习方法，以指导神经网络从时空变化中学习。我们在临床数据集中显示了我们方法的功效，该数据集由70名患者组成，每位患者进行一项随访研究。我们的结果表明，纵向数据中的时空信息是改善分割的有益提示。就总体得分而言，我们将当前最新的结果提高了2.6％（p <0.05）。代码公开可用。
Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temporal changes. We show the efficacy of our method on a clinical dataset comprised of 70 patients with one follow-up study for each patient. Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation. We improve the result of current state-of-the-art by 2.6% in terms of overall score (p<0.05). Code is publicly available.
