Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the in-depth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size, irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently, some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally, objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.