论文标题
癫痫深脑刺激程序的自动目标定位方法的多评价比较研究
A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures
论文作者
论文摘要
癫痫是第四大最常见的神经系统疾病，会影响全世界所有年龄段的人。当抗癫痫药或切除手术不能导致令人满意的结果时，深脑刺激（DBS）已成为一种替代治疗选择。为了促进程序及其标准化的规划，希望开发出一种算法以自动定位DBS刺激目标，即丘脑（ANT）的前核，这是一个具有挑战性的计划。在这项工作中，我们通过对ANT-DB的各种定位方法进行基准测试，进行了广泛的比较研究。具体而言，这项研究涉及的方法包括传统的注册方法和基于深度学习的方法，包括热图匹配和可区分的空间与数值变换（DSNT）。我们的实验结果表明，经过伪标签训练的基于深度学习（DL）的定位方法可以实现与评分者间和评估者内变异性相当的性能，并且它们比传统方法快的数量级。
Epilepsy is the fourth most common neurological disorder and affects people of all ages worldwide. Deep Brain Stimulation (DBS) has emerged as an alternative treatment option when anti-epileptic drugs or resective surgery cannot lead to satisfactory outcomes. To facilitate the planning of the procedure and for its standardization, it is desirable to develop an algorithm to automatically localize the DBS stimulation target, i.e., Anterior Nucleus of Thalamus (ANT), which is a challenging target to plan. In this work, we perform an extensive comparative study by benchmarking various localization methods for ANT-DBS. Specifically, the methods involved in this study include traditional registration method and deep-learning-based methods including heatmap matching and differentiable spatial to numerical transform (DSNT). Our experimental results show that the deep-learning (DL)-based localization methods that are trained with pseudo labels can achieve a performance that is comparable to the inter-rater and intra-rater variability and that they are orders of magnitude faster than traditional methods.
