Research Progress on Transformer-Based Deep Learning Models for Medical Image Segmentation
Accurate segmentation of medical images is a crucial step in clinical diagnosis and treatment.Over the past decade,convolutional neural network(CNN)has been widely applied in the field of medical image segmentation and have achieved excellent segmentation performance.However,the inherent inductive bias in CNN architectures limits their ability to model long-range dependencies in images.In contrast,the Transformer architectures,which focus on global information and the ability to model long-range dependencies,has been demonstrated outstanding performance in biomedical image segmentation.This review introduced the components of Transformer architecture and its applications in medical image segmentation.From perspectives of fully supervised,unsupervised and semi-supervised learning,application values and performances of Transformer architectures in abdominal multi-organ segmentation,cardiac segmentation and brain tumor segmentation were summarized and analyzed.Finally,limitations of Transformer model in segmentation tasks and future optimizations were prospected.
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