Abstract: Aiming at the shortcomings of existing methods in the image segmentation performance of small and medium-sized organs in the abdomen, we proposed  a network model based on local and global parallel coding  for multi-organ image segmentation in the abdomen. Firstly, a local coding branch was designed to extract multi-scale feature information. Secondly, the global feature coding branch adopted the  block Transformer, which not only captured the global long distance dependency information but also reduced the computation amount through the combination of intra-block Transformer and inter-block Transformer. Thirdly, a feature fusion module was designed to fuse the context information from two coding branches. Finally, the decoding module was designed to realize the interaction between global information and local context information, so as to better compensate for the information 
loss in the decoding stage. Experiments were conducted on the Synapse multi-organ CT dataset, compared with the current nine advanced methods, the average Dice similarity  coefficient  (DSC) and Hausdorff distance (HD) indicators achieve the best performance, with 83.10% and 17.80 mm, respectively.

Key words: multi-organ image segmentation, block Transformer, feature fusion