Local-Global Progressive U-Transformers for Accurate Hepatic and Portal Veins Segmentation in Abdominal MR Images

Segmentation of hepatic and portal veins in abdominal magnetic resonance images plays an essential role in surgical planning of liver tumor ablation and resection. Accurately extracting these blood vessels is a challenging task due to the complex vessel structures with high noise and irregular vessel shapes caused by nearby tumors. This work presents a new deep learning method called local-global progressive U-Transformers for precise extraction of hepatic and portal veins. Specifically, our method embeds convolution into the Transformer frame to extract features progressively and uses window attention to achieve full fusion of local and global features, as well as it only requires a small number of training parameters close to lightweight networks. We evaluated the proposed method on 30 clinical abdominal scans, with the experimental results showing that our method works better than the other segmentation approaches, improving the dice similarity coefficient from 0.7885 to 0.8132 and significantly reducing the number of parameters from 93.19M to 7.57M. We also found that our method can address the problem of voxel intensity variations and irregular vessel structures.