Hippocampus Segmentation

Hippocampus segmentation, the automated identification of this brain region in medical images, is crucial for diagnosing neurological disorders. Current research focuses on improving segmentation accuracy and efficiency using deep learning models, particularly U-Net and Transformer architectures, often employing parameter-efficient fine-tuning techniques like low-rank adaptation to address data scarcity and computational constraints. These advancements are driven by the need for robust and reliable methods to analyze hippocampal volume and shape, ultimately aiding in the diagnosis and monitoring of conditions like Alzheimer's disease.