SuperFormer: Unet‐Like Super Token Transformer for Medical Image Segmentation

The application of computer‐aided diagnosis in the medical field is gradually becoming widespread. Multi‐organ segmentation in clinical abdominal CT images and cardiac MRI images poses a challenging task. Accurate segmentation of multiple organs is a crucial prerequisite for disease diagnosis and treatment planning. In this paper, we introduce a multi‐organ segmentation method based on CT or MRI images: SuperFormer.SuperFormer is a hierarchical encoder‐decoder network with two compelling designs: (1) It introduces the super token transformer block into the U‐shaped encoder‐decoder structure, making it easier to extract global information while significantly improving computational efficiency. (2) It presents a channel‐based multi‐scale Transformer context bridge for effectively extracting correlations of global dependencies and local context in multi‐scale features generated by our hierarchical Transformer encoder. This guides the efficient connection of fused multi‐scale channel information to decoder features, eliminating the semantic gap. In medical image segmentation, SuperFormer demonstrates a powerful ability to capture more discriminative dependencies and context. Experimental results on multi‐organ segmentation and cardiac segmentation tasks demonstrate the algorithm's superiority, effectiveness, and robustness. Specifically, experimental results from training SuperFormer from scratch even surpass state‐of‐the‐art methods pretrained on ImageNet, and its core design can be extended to other visual segmentation tasks.