Integrating Vision Transformer with UNet++ for Hippocampus Segmentation in Alzheimer's Disease

The hippocampus is a disease-prone area of the brain that can be used as an important biomarker for neurodegenerative diseases like Alzheimer's. In recent years, deep neural networks have been applied to segment the hippocampus. However, accurately segmenting the hippocampus using magnetic resonance imaging (MRI) remains a challenging task. To explore a more effective segmentation strategy, this study proposes a new model by integrating the Vision Transformer (ViT) architecture with the UNet++ architecture, which is validated by using manual tracing of the hippocampus performed by clinical experts. The proposed ViT-based model achieved a dice score of 0.885, surpassing similar models by 2.82% in the Dice coefficient score.