A dual-branch encoder network based on squeeze-and-excitation UNet and transformer for 3D PET-CT image tumor segmentation.
Authors
Affiliations (3)
Affiliations (3)
- College of Medicine and Biomedical Information Engineering, Northeastern University, 110169, Shenyang, China.
- College of Medicine and Biomedical Information Engineering, Northeastern University, 110169, Shenyang, China. [email protected].
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, 110169, Shenyang, China. [email protected].
Abstract
Recognition of tumors is very important in clinical practice and radiomics; however, the segmentation task currently still needs to be done manually by experts. With the development of deep learning, automatic segmentation of tumors is gradually becoming possible. This paper combines the molecular information from PET and the pathology information from CT for tumor segmentation. A dual-branch encoder is designed based on SE-UNet (Squeeze-and-Excitation Normalization UNet) and Transformer, 3D Convolutional Block Attention Module (CBAM) is added to skip-connection, and BCE loss is used in training for improving segmentation accuracy. The new model is named TASE-UNet. The proposed method was tested on the HECKTOR2022 dataset, which obtains the best segmentation accuracy compared with state-of-the-art methods. Specifically, we obtained results of 76.10 <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>%</mo></math> and 3.27 for the two key evaluation metrics, DSC and HD95. Experiments demonstrate that the designed network is reasonable and effective. The full implementation is available at https://github.com/LiMingrui1/TASE-UNet .