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

• Published in: Human brain mapping Vol. 42; no. 9; pp. 2862 - 2879
• Main Authors: Solomon, Oren, Palnitkar, Tara, Patriat, Re'mi, Braun, Henry, Aman, Joshua, Park, Michael C., Vitek, Jerrold, Sapiro, Guillermo, Harel, Noam
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.06.2021
• Subjects: 7 Tesla magnetic resonance imaging; Adult; Aged; Aged, 80 and over; Basal ganglia; Computational linguistics; convolutional neural network; Deep brain stimulation; Deep Learning; Disorders; Dystonia; Female; Ganglia; Globus pallidus; Globus Pallidus - anatomy & histology; Globus Pallidus - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Image Interpretation, Computer-Assisted - standards; Image resolution; Image segmentation; Language processing; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; Mediation; Medical imaging equipment; Middle Aged; Movement disorders; Movement Disorders - diagnostic imaging; Movement Disorders - pathology; Natural language interfaces; Neural networks; Neurodegenerative diseases; Parkinson's disease; patient‐specific; Quality of life; Reproducibility of Results; segmentation; Signs and symptoms; Surgery; Target detection; Target recognition; Tremor; Young Adult; patient-specific; Parkinson's disease; deep-learning; 7 Tesla magnetic resonance imaging; convolutional neural network; segmentation; globus pallidus; deep brain stimulation
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25409

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. Deep brain stimulation (DBS) surgery has been shown to improve the quality of life for patients with various motor dysfunctions. The success of DBS is directly related to the proper placement of the electrodes, which requires accurate detection and identification of the relevant target structures. We present a deep‐learning based automatic, robust and accurate segmentation technique from 7 Tesla MRI acquisitions of the globus pallidus externa and interna for DBS surgery planning and postoperative electrode localization.