  The CNN-based methods have achieved impressive results in medical image
segmentation, but they failed to capture the long-range dependencies due to the
inherent locality of the convolution operation. Transformer-based methods are
recently popular in vision tasks because of their capacity for long-range
dependencies and promising performance. However, it lacks in modeling local
context. In this paper, taking medical image segmentation as an example, we
present MISSFormer, an effective and powerful Medical Image Segmentation
tranSFormer. MISSFormer is a hierarchical encoder-decoder network with two
appealing designs: 1) A feed-forward network is redesigned with the proposed
Enhanced Transformer Block, which enhances the long-range dependencies and
supplements the local context, making the feature more discriminative. 2) We
proposed Enhanced Transformer Context Bridge, different from previous methods
of modeling only global information, the proposed context bridge with the
enhanced transformer block extracts the long-range dependencies and local
context of multi-scale features generated by our hierarchical transformer
encoder. Driven by these two designs, the MISSFormer shows a solid capacity to
capture more discriminative dependencies and context in medical image
segmentation. The experiments on multi-organ and cardiac segmentation tasks
demonstrate the superiority, effectiveness and robustness of our MISSFormer,
the experimental results of MISSFormer trained from scratch even outperform
state-of-the-art methods pre-trained on ImageNet. The core designs can be
generalized to other visual segmentation tasks. The code has been released on
Github: https://github.com/ZhifangDeng/MISSFormer
