CTUNet: automatic pancreas segmentation using a channel-wise transformer and 3D U-Net

• Published in: The Visual computer Vol. 39; no. 11; pp. 5229 - 5243
• Main Authors: Chen, Lifang, Wan, Li
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Abdomen; Artificial Intelligence; Computer Graphics; Computer Science; Datasets; Image Processing and Computer Vision; Image segmentation; Medical imaging; Medical research; Methods; Modules; Natural language processing; Neural networks; Original Article; Pancreas; Success; Transformers; Deep learning; Auto pancreas segmentation; 3D channel transformer; 3D U-Net
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-022-02656-2

Diabetes, pancreatic cancer, and pancreatitis are all diseases of the pancreas, which seriously threaten people’s lives. The pancreas has a special anatomical structure, its size, shape, and position are variable, and it is highly similar to other surrounding deep abdominal tissues, so achieving accurate segmentation is still one of the most challenging tasks in the field of medical image segmentation. We propose a new network CTUNet that combines Transformer and 3D U-Net, which can achieve high-precision automatic segmentation of the pancreas. We deploy the Transformer on skip connections to coordinate global explicit features and guide the network learning. In view of pancreas reciprocity and shape variability, we design a Pancreas Attention module and add it to each encoder to further enhance the ability to extract context information and learn distinct features. In addition, in the decoder, we use a novel Feature Concatenation module with an attention mechanism to further promote the fusion of different levels of features and alleviate the problem of loss of down-sampling feature information. We train and test our model on the NIH dataset and evaluate with Dice Similarity Coefficient, Jaccard Index, Precision, and Recall. Experimental results show that our proposed model outperforms most existing pancreas segmentation methods.