A multi-rater comparative study of automatic target localization methods for epilepsy deep brain stimulation procedures

Han Liu; Kathryn L. Holloway; Dario J. Englot; Benoit M. Dawant

doi:10.1117/12.2612927

4 April 2022 A multi-rater comparative study of automatic target localization methods for epilepsy deep brain stimulation procedures

Han Liu, Kathryn L. Holloway, Dario J. Englot, Benoit M. Dawant

Author Affiliations +

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120342C (2022) https://doi.org/10.1117/12.2612927
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

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 respective 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

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Han Liu, Kathryn L. Holloway, Dario J. Englot, and Benoit M. Dawant "A multi-rater comparative study of automatic target localization methods for epilepsy deep brain stimulation procedures", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120342C (4 April 2022); https://doi.org/10.1117/12.2612927

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