A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures

TL;DR

This study compares traditional and deep-learning-based methods for automatically localizing the target in epilepsy deep brain stimulation, demonstrating that DL methods are as accurate as human raters and significantly faster.

Contribution

It provides a comprehensive benchmark of localization techniques, highlighting the effectiveness and efficiency of deep-learning approaches for ANT localization in DBS procedures.

Findings

Deep-learning methods achieve performance comparable to human raters.

DL methods are significantly faster than traditional registration techniques.

Pseudo label training enables effective deep-learning localization.

Abstract

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.