Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI.

Journal Article (Journal Article)

Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this study, we present a deep-learning based neural network, which we term GP-net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP-net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP-net performs 3D inference in a patient-specific manner, alleviating the need for atlas-based segmentation. GP-net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state-of-the-art atlas-based segmentations. We also demonstrate a postoperative lead location assessment with respect to a segmented globus pallidus obtained by GP-net.

Full Text

Duke Authors

Cited Authors

  • Solomon, O; Palnitkar, T; Patriat, R; Braun, H; Aman, J; Park, MC; Vitek, J; Sapiro, G; Harel, N

Published Date

  • June 2021

Published In

Volume / Issue

  • 42 / 9

Start / End Page

  • 2862 - 2879

PubMed ID

  • 33738898

Pubmed Central ID

  • PMC8127160

Electronic International Standard Serial Number (EISSN)

  • 1097-0193

International Standard Serial Number (ISSN)

  • 1065-9471

Digital Object Identifier (DOI)

  • 10.1002/hbm.25409

Language

  • eng