Semi-supervised 3D Medical Image Segmentation Using Transformer and CNN
Zhipeng Zhao, Jianjun Zhang, Peishun Liu, Hanqing Liu, Yixin Chen, Charles Li, Xianchang Liu, Ruichun Tang · 2023
Due to the lack of labeled information in medical images, semi-supervised learning has been highly valued in the field of image segmentation. How to effectively use unlabeled images to guide image segmentation is regarded as a key issue to achieve accurate segmentation. In this paper, an uncertainty-aware based consistent segmentation method is proposed to fully utilize the power of Transformer and Convolution Neural Network (CNN) in semi- supervised image segmentation. Our proposed framework consists of a feature learning module which is enhanced by Transformer and CNN, and a feature guidance module based on consistency perception. In the feature learning module, Transformer is used as an encoder to extract multi-scale features of labeled images, and CNN is used as a decoder to restore image dimensions. The feature-guided module learns the features of unlabeled images after data perturbation, and develops a feature-guided model by averaging network weights. In this paper, medical image segmentation based on consensus perception is implemented in a semi-supervised manner, and the proposed method achieves good performance on public benchmark datasets.