Abstract
The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to its low contrast and tiny size, and the manual segmentation results vary greatly depending on the observer. Therefore, there is a great need for automatic quantitative analytic methods of the habenula for psychiatric research purposes. Here we propose an automated segmentation and volume estimation method for the habenula in 7 Tesla magnetic resonance imaging based on a deep learning-based semantic segmentation network. The proposed method, using the data of 69 participants (33 patients with MDD and 36 normal controls), achieved an average precision, recall, and dice similarity coefficient of 0.869, 0.865, and 0.852, respectively, in the automated segmentation task. Moreover, the intra-class correlation coefficient reached 0.870 in the volume estimation task. This study demonstrates that this deep learning-based method can provide accurate and quantitative analytic results of the habenula. By providing rapid and quantitative information on the habenula, we expect our proposed method will aid future psychiatric disease studies.
Highlights
The habenula (Hb) is a paired epithalamic structure adjacent to the dorsomedial thalamus and the third ventricle[1] that can be divided into distinct portions via different cellular morphological features
Based on previous studies of Hb function, the Hb is involved in the pathogenesis of psychiatric disorders such as major depressive disorder (MDD)[3,4]
The intra-class correlation coefficients (ICCs) between automatic and manual segmentation of the total Hb were excellent in all participants, participants with MDD, and normal controls (NCs)
Summary
The habenula (Hb) is a paired epithalamic structure adjacent to the dorsomedial thalamus and the third ventricle[1] that can be divided into distinct portions via different cellular morphological features. An accurate and quick Hb segmentation method might be a fundamental step in medical treatment, such as deep brain stimulation and neurosurgery, for targeting Hb sub-regions related to psychiatric diseases in the future[12,13] For this reason, a couple of semi- or fully-automatic Hb segmentation approaches have been reported: (1) reproducibility of a myelin content-based Hb segmentation from 3 T magnetic resonance imaging (MRI) using a semi-automatic myelin contrast-based method[14], and (2) a machine learning algorithm for fully-automatic Hb segmentation of 1.5 T MRI for Hb volume comparison of patients with bipolar disorder and schizophrenia with healthy controls[15]. The final Hb segmentation results fused the two pre-trained networks’ outputs, taking into account both examiners’ manually segmented masks
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