  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.
