MAST-net: 3D image segmentation of hippocampus based on multiple attention serial mechanisms

As the global population ages, neurodegenerative diseases such as Alzheimer's disease pose a serious threat to the health and quality of life of the elderly population[1].Studies have shown that shrinkage of hippocampal volume is closely associated with the emergence of diseases such as Alzheimer's disease, mild cognitive impairment and temporal lobe epilepsy. Therefore, accurate segmentation of the hippocampus has become a critical step in the diagnosis and study of these diseases. Aiming at the segmentation difficulties caused by the characteristics of the hippocampus, such as irregular shape, small volume and fuzzy edges, this paper proposes a deep learning-based hippocampus segmentation method.This method combines sequence learning and U-networks, and proposes a module based on multiple attention serial mechanisms (MAST), which incorporates the dependency information between image sequences into a 3D semantic segmentation network by introducing sequence learning to fully utilize the 3D contextual information of images. In addition, for the sample balancing problem, this paper incorporates a multi-layer decoupling mechanism (MLDM-multi-layer decoupling mechanism) in the jump-connection stage to improve the segmentation effect. Experiments were conducted on the Task04_Hippocampus dataset to verify the performance and stability of the method. The results of the comparison experiments with normal networks show that the introduction of sequence learning structure significantly improves the segmentation effect. All in all, the hippocampus segmentation method proposed in this paper not only improves the segmentation accuracy, but also provides strong support for the clinical diagnosis of neurodegenerative diseases. Future work will further optimize the algorithm to improve the segmentation efficiency and explore its potential application in the diagnosis of more diseases.