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| The hippocampus is located between the thalamus and the medial temporal lobe. It is mainly responsible for cognition, learning, and long and short memory. It is closely related to many diseases such as Alzheimer's disease and temporal lobe epilepsy. Therefore, the accurate segmentation of the hippocampal structure in magnetic resonance imaging is of great significance for the diagnosis of brain injury and brain disease prediction in clinical medicine. In recent years, the rapid development of deep learning technology has brought about brand-new changes to the field of hippocampal segmentation. Deep learning is data-driven, and the quantity and quality of data directly affect the accuracy of hippocampal segmentation. However, due to the difficulty of MR imaging acquisition and expensive manual annotation, hippocampus MR imaging is relatively scarce, which limits the performance improvement of deep learning models in hippocampal segmentation tasks to some extent. In order to overcome the challenges in small sample data scenarios and improve the accuracy of hippocampal segmentation, this paper proposes a data augmentation method, which aims to expand the data (brain magnetic resonance images) and label (hippocampus mask) simultaneously, so as to alleviate the problem of data scarcity and annotation scarcity. Through experiments, the proposed method can effectively improve the accuracy of hippocampal segmentation. |
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| Keywords:Artificial Intelligence; hippocampal segmentation; deep learning; data augmentation;small datasets |
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