Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe

• Published in: Magnetic resonance imaging 2021-09, Vol.81, p.17-23
• Main Authors: Plassard, Andrew J., Bao, Shunxing, McHugo, Maureen, Beason-Held, Lori, Blackford, Jennifer U., Heckers, Stephan, Landman, Bennett A.
• Format: Article
• Language: English
• Subjects: Amygdala; Amygdala - diagnostic imaging; Hippocampus; Hippocampus - diagnostic imaging; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Multi-atlas segmentation; Reproducibility of Results; Temporal lobe; Temporal Lobe - diagnostic imaging
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/j.mri.2021.04.011

Examining volumetric differences of the amygdala and anterior-posterior regions of the hippocampus is important for understanding cognition and clinical disorders. However, the gold standard manual segmentation of these structures is time and labor-intensive. Automated, accurate, and reproducible techniques to segment the hippocampus and amygdala are desirable. Here, we present a hierarchical approach to multi-atlas segmentation of the hippocampus head, body and tail and the amygdala based on atlases from 195 individuals. The Open Vanderbilt Archive of the temporal Lobe (OVAL) segmentation technique outperforms the commonly used FreeSurfer, FSL FIRST, and whole-brain multi-atlas segmentation approaches for the full hippocampus and amygdala and nears or exceeds inter-rater reproducibility for segmentation of the hippocampus head, body and tail. OVAL has been released in open-source and is freely available. •Present labeling protocols for the hippocampus head, body and amygdala.•created an atlas population of 195 subjects with manually traced hippocampi and automatically segmented amygdalae•presented the OVAL algorithm which is a hierarchical approach for the the full hippocampus and amygdala segmentation