Reproducibility and Clinical Validation of Automated Habenula Segmentation via deep Learning in Major Depressive Disorder with 7 Tesla MRI [post]

Sang-Heon Lim, Jihyun Yoon, Young Jae Kim, Chang-Ki Kang, Seo-Eun Cho, Kwang Gi Kim, Seung-Gul Kang
2021 unpublished
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
more » ... poses. Here we propose a fully-automated segmentation and volume estimation method for the habenula in 7 Tesla magnetic resonance imaging based on a novel fully convolutional network. The proposed method, using the data of 69 participants (33 patients with MDD and 36 normal controls), achieved an average precision, sensitivity, and dice similarity coefficient of 0.869, 0.865, and 0.852, respectively, in the automated segmentation task. Moreover, the intraclass 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.
doi:10.21203/rs.3.rs-170039/v1 fatcat:3ggobbyxbvaetcbyvi5tl6vf5a