基于多模态融合和自适应剪枝Transformer的脑肿瘤图像分割算法OACHSSCDCSTPCD

Brain tumors are one of the most lethal tumors in the world,so automatic segmentation of brain tumor images is becoming increasingly important in clinical diagnosis and treatment.In recent years,brain tumor segmentation methods based on CNN and Transformer have achieved gratifying achievements in the field of medical image segmentation.However,in most methods the complementarity and difference between brain tumor multimodalities are not fully exploited,and in methods with Transformer where long-range dependence is captured,the large computational complexity and redundant dependencies are unbearable.To solve the two problems,a brain tumor image segmentation method(MF-MAPT Swin UNETR)based on multi-modal fusion and adaptive pruning Transformer is proposed.The multi-modal fusion module can fully learn the charac-teristics between modalities with similar properties and feature changes in different modes and scales provide suf-ficient preparation for subsequent segmentation;the multi-modal adaptive pruning Transformer can reduce com-putational complexity and help improve performance.The model is experimentally verified on two public datas-ets,and it is shown by the experimental results that the proposed MF-MAPT Swin UNETR model exhibits outstanding segmentation performance overall compared with state-of-the-art methods.