Reproducibility of myelin content‐based human habenula segmentation at 3 Tesla. Issue 7 (26th March 2018)

• Record Type: Journal Article
• Authors: Kim, Joo‐Won; Naidich, Thomas P.; Joseph, Joshmi; Nair, Divya; Glasser, Matthew F.; O'halloran, Rafael; Doucet, Gaelle E.; Lee, Won Hee; Krinsky, Hannah; Paulino, Alejandro; Glahn, David C.; Anticevic, Alan; Frangou, Sophia; Xu, Junqian
• Is Part Of: Human brain mapping. Volume 39:Issue 7(2018)
• Page Start: 3058
• Page End: 3071
• Publication Date: 2018-03-26
• Subjects: Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24060
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi‐automated habenula segmentation methods have been reported, the test‐retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra‐ and inter‐site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7–0.8 mm isotropic resolution) using our previously proposed semi‐automated myelin contrast‐based method and its fully‐automated version, as well as a previously published manual geometry‐based method. The habenula segmentation using our semi‐automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi‐automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi‐automated habenula segmentation showed reliable and reproducible habenula localization, while its fully‐automated version offers an efficient way for large sample analysis.