Automated U-Net Hippocampal Segmentation and Volumetric Classification for Major Depressive Disorder Stage Differentiation

Abstract

Major Depressive Disorder (MDD) is the leading cause of disability in the world, affecting approximately 280 million people. Hippocampal volumetric changes have been proposed as a potential biomarker for depression. Despite advancements in neuroimaging studies related to psychiatric disorders, there remains a gap in the utilization of neuroimaging techniqes for clinical diagnosis and monitoring of such disorders. This study presents a comprehensive investigation of Major Depressive Disorder (MDD) stage differentiation using MRI data and a U-Net architecture for hippocampal segmentation across axial, coronal, and sagittal orientations. This approach presents a U-Net architecture to segment 2D slices of the hippocampus from MRI data in axial, sagittal, and coronal orientations. These segmented 2D slices are then concatenated to create a hippocampus volume representation, which is then used to obtain the hippocampal volume. These volumetric values are subsequently used to differentiate among the four stages of MDD according to the Beck Depression Inventory-II (minimal/no MDD, mild, moderate, and severe) using a Neural Network and three machine learning classifiers. The experimental results demostrated that the U-Net model effectively segments the hippocampus, while the volumetric analysis accurately differentiates MDD stages. The Gradient Boost classifier outperformed the other classifiers with an 98.5% accuracy, 99.90% precision, 98.85% recall, and an F1-score of 98.6%. This study advances the field of neuroimaging and mental disorder assessment by introducing a reliable and automated method for hippocampus segmentation and MDD stage categorization. Future directions include incorporating more brain regions such as the amygdala and habenula, creating a neural networks classifier based on 3D hippocampus images, and using larger, more diverse datasets to increase model performance and generalizability.