Semi-supervised Liver CT Image Segmentation Based on Cross-task Consistency
Current deep learning-based medical image segmentation methods often require large amounts of labeled data to train network models.However,labeled data acquisition for medical images is usually quite expensive,and semi-supervised learning enables models to learn using large amounts of unlabeled data and small amounts of labeled data.In this paper,a semi-supervised learning framework based on cross-task consistency is proposed to reduce the cost of labeled data required for neural network model training.The method utilizes the V-Net network as the backbone framework and adds two auxiliary decoders,while introducing an auxiliary regression task in the decoder to improve the model segmentation performance and imposing a regularization-constrained cross-task consistency loss between the segmentation and regression tasks of the primary and secondary decoders,which is able to learn a large amount of unlabeled data geometric prior information.We validate the effectiveness of the proposed method on the LiTS2017 Challenges dataset.The Dice coefficient and Jaccard index of the proposed method reached 93.95%and 88.87%,respectively,in the experiments using 20%labeled data,which increased by 3.60 percentage points and 5.78 percentage points compared with the fully supervised V-Net network model training.The experimental results show that the proposed method achieves the accuracy of segmenting the liver with a small amount of la-beled data close to 100%labeled data training,and the segmentation accuracy is better compared with other semi-supervised methods.
medical imagingsemi-supervised learningneural networksregression tasksconsistency loss
万方数据
维普
