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Proceedings Paper

Localisation of the subthalamic nucleus in MRI via convolutional neural networks for deep brain stimulation planning
Author(s): John S. H. Baxter; Ehouarn Maguet; Pierre Jannin
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Paper Abstract

Deep brain stimulation (DBS) is an interventional treatment for Parkinson’s disease in which electrodes are placed into specific locations in the basal ganglia to alleviate symptoms such as tremor and dyskinesia. Due to the small size and low contrast of specific targets of interest, such as the subthalamic nucleus (STN), localisation of these structures from pre-operative MRI is of great value. These localisation approaches are often atlas-based, using registration algorithms to align patient images with a prior annotated atlas. However, these methods require a large amount of time, especially for correctly estimating deformation fields, or are prone to error for smaller structures such as the STN. This paper investigates two deep learning frameworks for the localisation of the stn from T1- and T2-weighted MRI using convolutional neural networks which estimate its centroid. These methods are compared against an atlas-based segmentation using the ParkMedAtlis v3 atlas, showing an improvement in localisation error in the range of ≈0.5-1.3 mm with a reduction of orders of magnitude of computation time. This method of STN localisation will allow us in future to automatically identify the STN for DBS surgical planning as well as define a greatly reduced region-of-interest for more accurate segmentation of the STN.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150M (16 March 2020); doi: 10.1117/12.2548420
Show Author Affiliations
John S. H. Baxter, Univ. Rennes, INSERM, LTSI (France)
Ehouarn Maguet, Univ. Rennes, INSERM, LTSI (France)
Pierre Jannin, Univ. Rennes, INSERM, LTSI (France)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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