Abdominal multi-organ segmentation in CT using Swinunter
Mingjin Chen Yongkang He Yongyi Lu
Abdominal multi-organ segmentation in computed tomography (CT) is crucial for
many clinical applications including disease detection and treatment planning.
Deep learning methods have shown unprecedented performance in this perspective.
However, it is still quite challenging to accurately segment different organs
utilizing a single network due to the vague boundaries of organs, the complex
background, and the substantially different organ size scales. In this work we
used make transformer-based model for training. It was found through previous
years' competitions that basically all of the top 5 methods used CNN-based
methods, which is likely due to the lack of data volume that prevents
transformer-based methods from taking full advantage. The thousands of samples
in this competition may enable the transformer-based model to have more
excellent results. The results on the public validation set also show that the
transformer-based model can achieve an acceptable result and inference time.