Multi-Modal MR Image Segmentation Strategy for Brain Tumors Based on Domain Adaptation

During the study of multimodal brain tumor MR image segmentation, the large differences in the image distributions make the assumption that the conditional probabilities are similar when the edge distributions of the target and source domains are similar, and that the edge distributions are similar when the conditional probabilities are similar, not valid. In addition, the training network is usually trained on single domain data, which creates a tendency for the network to represent the image towards the source domain when the target domain is not labeled. Based on the aforementioned reasons, a new multimodal brain tumor MR segmentation strategy based on domain adaptation is proposed in this study. First, the source domain targets for each modality are derived through the clustering methods in the pre-training stage to select the target domain images with the strongest complementarity in the source domain and further produce the pseudo labels. Second, feature adapters are proposed to improve the feature alignment, and a network sensitive to both source and target domain images is designed to comprehensively leverage the multimodal image information. These measures mitigate the domain shift problem and improve the generalization ability of the model, enhancing the accuracy of multimodal brain tumor MR image segmentation.