Automated Slice Selection and Labeling of Hippocampus Regions for Alzheimer's Disease

Alzheimer's disease image analysis is a challenging task due to the limited availability of labeled data. Labeling data requires precision and patience and typically done manually by medical experts. However, human labeling is expensive, time-consuming, and prone to human errors. To address this issue, we propose an approach that automates the selection and labeling process using a deep learning model. Specifically, we employ YOLOv7 to reduce time and errors. In this work, we implement our proposed idea on 2D images from the Alzheimer's dataset. This work focuses on labeling the hippocampus region from the coronal view of brain MRI images. The results of our experiments show that our approach yields result that are quite close to the ground truth labels produced by experts. The mean average precision (mAP) compare with ground truth is about 2% below, while the time consumption is reduced by 80%.