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

Posted by Bao Shunxing on Saturday, August 28, 2021 in Big Data, Deep Brain Stimulation, Image Processing, Image Segmentation, Machine Learning, Magnetic resonance imaging, Multi-atlas Segmentation, Neuroimaging, Reproducibility.

Plassard, Andrew J., Shunxing Bao*, Maureen McHugo, Lori Beason-Held, Jennifer U. Blackford, Stephan Heckers, and Bennett A. Landman. “Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe.” Magnetic Resonance Imaging 81 (2021): 17-23.

Full text: https://www.sciencedirect.com/science/article/abs/pii/S0730725X21000692

Abstract

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 at https://www.nitrc.org/projects/oval/.