Moment-Consistent Contrastive CycleGAN for Cross-Domain Pancreatic Image Segmentation

• Published in: IEEE transactions on medical imaging Vol. PP; p. 1
• Main Authors: Chen, Zhongyu, Bian, Yun, Shen, Erwei, Fan, Ligang, Zhu, Weifang, Shi, Fei, Shao, Chengwei, Chen, Xinjian, Xiang, Dehui
• Format: Journal Article
• Language: English
• Published: United States IEEE 21.08.2024
• Subjects: Computed tomography; contrastive learning; Domain adaptation; Feature extraction; Generative adversarial networks; Image segmentation; Image transformation; moment-consistent; Pancreas; Shape
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2024.3447071

CT and MR are currently the most common imaging techniques for pancreatic cancer diagnosis. Accurate segmentation of the pancreas in CT and MR images can provide significant help in the diagnosis and treatment of pancreatic cancer. Traditional supervised segmentation methods require a large number of labeled CT and MR training data, which is usually time-consuming and laborious. Meanwhile, due to domain shift, traditional segmentation networks are difficult to be deployed on different imaging modality datasets. Cross-domain segmentation can utilize labeled source domain data to assist unlabeled target domains in solving the above problems. In this paper, a cross-domain pancreas segmentation algorithm is proposed based on Moment-Consistent Contrastive Cycle Generative Adversarial Networks (MC-CCycleGAN). MC-CCycleGAN is a style transfer network, in which the encoder of its generator is used to extract features from real images and style transfer images, constrain feature extraction through a contrastive loss, and fully extract structural features of input images during style transfer while eliminate redundant style features. The multi-order central moments of the pancreas are proposed to describe its anatomy in high dimensions and a contrastive loss is also proposed to constrain the moment consistency, so as to maintain consistency of the pancreatic structure and shape before and after style transfer. Multi-teacher knowledge distillation framework is proposed to transfer the knowledge from multiple teachers to a single student, so as to improve the robustness and performance of the student network. The experimental results have demonstrated the superiority of our framework over state-of-the-art domain adaptation methods.