  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 semantic
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 outstanding 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.
The code will be available at https://github.com/goblin327/SegStitch
