MISSFormer: An Effective Medical Image Segmentation Transformer

By Xiaohong Huang, Dandan Li, Xueguang Yuan. arXiv.org, 2021

302 citations recorded by the source corpus.

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

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f75bf767d060785e553326627adfee77f8e19d86

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Qian Wang
  2. Ehsan Khodapanah Aghdam
  3. Dinggang Shen
  4. Dorit Merhof
  5. Reza Azad
  6. Zihao Yin
  7. Wen Ji
  8. Junfeng Zhang

In Radiology, Nuclear Medicine and Imaging

  1. Ehsan Khodapanah Aghdam
  2. Dinggang Shen
  3. Dorit Merhof
  4. Reza Azad
  5. Qian Wang
  6. Islem Rekik

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