Towards Standardized BNST Segmentation: A Deep Learning Model for Precise and Reproducible 3D T1 MRI Analysis

Motivation: Segmenting the bed nucleus of the stria terminalis (BNST) is vital for understanding its role in stress and anxiety. Due to its small size and indistinct boundaries, manual segmentation is challenging, making automated deep learning methods beneficial. Goal(s): Our goal was to develop and evaluate a deep learning model using nn-UNet for the automatic segmentation of the BNST. Approach: Using 48 3D T1-weighted scans, 35 trained the model, and 13 evaluated its performance. Results: The model achieved a Dice score of 0.83, an IoU of 0.71, an MCC of 0.83, suggesting that the model has a high specificity identifying the BNST. Impact: This model enables automatic, precise BNST segmentation, advancing research on stress-related brain pathways, supporting personalized treatments, and enhancing diagnostic accuracy in psychiatric conditions. Automated BNST analysis facilitates early detection, improves patient outcomes, and accelerates large-scale research possibilities.