3-D contour-aware U-Net for efficient rectal tumor segmentation in magnetic resonance imaging.
Authors
Affiliations (4)
Affiliations (4)
- Image Processing Center, Beihang University, Beijing, 102206, China.
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China; Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 40016, China.
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 40016, China. Electronic address: [email protected].
- Image Processing Center, Beihang University, Beijing, 102206, China; The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China; Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technology, Ministry of Education, Beihang University, Beijing, 102206, China. Electronic address: [email protected].
Abstract
Magnetic resonance imaging (MRI), as a non-invasive detection method, is crucial for the clinical diagnosis and treatment plan of rectal cancer. However, due to the low contrast of rectal tumor signal in MRI, segmentation is often inaccurate. In this paper, we propose a new three-dimensional rectal tumor segmentation method CAU-Net based on T2-weighted MRI images. The method adopts a convolutional neural network to extract multi-scale features from MRI images and uses a Contour-Aware decoder and attention fusion block (AFB) for contour enhancement. We also introduce adversarial constraint to improve augmentation performance. Furthermore, we construct a dataset of 108 MRI-T2 volumes for the segmentation of locally advanced rectal cancer. Finally, CAU-Net achieved a DSC of 0.7112 and an ASD of 2.4707, which outperforms other state-of-the-art methods. Various experiments on this dataset show that CAU-Net has high accuracy and efficiency in rectal tumor segmentation. In summary, proposed method has important clinical application value and can provide important support for medical image analysis and clinical treatment of rectal cancer. With further development and application, this method has the potential to improve the accuracy of rectal cancer diagnosis and treatment.