SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation

Authors: Shengbo Tan, Zeyu Zhang, Ying Cai*, Daji Ergu, Lin Wu, Binbin Hu, Pengzhang Yu, Yang Zhao

*Corresponding author

[Paper Link] [Papers With Code]

Medical imaging segmentation plays a significant role in the automatic recognition and analysis of lesions. State-of- the-art methods, particularly those utilizing transformers, have been prominently adopted in 3D semantic segmentation due to their superior performance in scalability and generalizability. However, plain vision transformers encounter challenges due to their neglect of local features and their high computational complexity. To address these challenges, we introduce three key contributions: Firstly, we proposed SegStitch, an innovative architecture that integrates transformers with denoising ODE blocks. Instead of taking whole 3D volumes as inputs, we adapt axial patches and customize patch-wise queries to ensure seman- tic consistency. Additionally, we conducted extensive experiments on the BTCV and ACDC datasets, achieving improvements up to 11.48% and 6.71% respectively in mDSC, compared to state-of- the-art methods. Lastly, our proposed method demonstrates out- standing efficiency, reducing the number of parameters by 36.7% and the number of FLOPS by 10.7% compared to UNETR. This advancement holds promising potential for adapting our method to real-world clinical practice.

Code will be released later. Stay tuned.

Citation

For academic use, please cite:

@article{tan2024segstitch,
  title={SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation},
  author={Tan, Shengbo and Zhang, Zeyu and Cai, Ying and Ergu, Daji and Wu, Lin and Hu, Binbin and Yu, Pengzhang and Zhao, Yang},
  journal={arXiv preprint arXiv:2408.00496},
  year={2024}
}

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