Segmentation and volume estimation of habenula using deep-learning in patients with depression

Background The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine the changes in healthy participants and patients with depression. Methods This multicenter study included 382 participants (patients with depression: n=234, women 47.0%; healthy participants: n=148, women 37.8%). A three-dimensional Residual U-Net was used to create a habenula segmentation model on 3 Tesla magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, the differences between the habenula volume of healthy participants and that of patients with depression were examined. Results A dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p<10-7; r=-0.59). The habenula volume decreased with severity in women with depression, even when the effects of age and scanner were excluded (p=0.019; η2=0.099). Conclusions The habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.