Domain shift is a significant challenge in medical image segmentation, primarily due to variations in image acquisition protocols, modalities, etc. Domain shift often causes models trained on a source domain to perform poorly on unseen target domains. In this work, we introduce the Domain-Generalized Discrete Diffusion Model for Segmentation (DG-DDM-Seg), a diffusion-based generative model designed for single-source domain generalization in medical image segmentation. DG-DDM-Seg generates discrete conditional distributions of segmentation masks. To ensure domain independence, we employ two key strategies: 1) We extract robust features from conditional images to enhance the domain independence of diffusion model. 2) We use both conditional images and pseudo-labels as inputs to improve cross-domain segmentation performance. Along this idea, we propose a two-path reverse diffusion process during training, utilizing Robust Feature Extraction Subnet and Mask-Generation Transformer to learn a domain-generalized discrete conditional distribution based on robust image features and pseudo-labels. This learned distribution is then used to generate segmentation masks for unseen target domains. Experimental results demonstrate that DG-DDM-Seg achieves state-of-the-art performance in cross-domain medical image segmentation, with domain shifts in modality, sequence, and site. The code is available at https://github.com/HeranYang/DG-DDM-Seg.

Accurate segmentation of brain tumors is of critical importance in clinical assessment and treatment planning, which requires multiple MR modalities providing complementary information. However, due to practical limits, one or more modalities may be missing in real scenarios. To tackle this problem, existing methods need to train multiple networks or a unified but fixed network for various possible missing modality cases, which leads to high computational burdens or sub-optimal performance. In this paper, we propose a unified and adaptive multi-modal MR image synthesis method, and further apply it to tumor segmentation with missing modalities. Based on the decomposition of multi-modal MR images into common and modality-specific features, we design a shared hyper-encoder for embedding each available modality into the feature space, a graph-attention-based fusion block to aggregate the features of available modalities to the fused features, and a shared hyper-decoder for image reconstruction. We also propose an adversarial common feature constraint to enforce the fused features to be in a common space. As for missing modality segmentation, we first conduct the feature-level and image-level completion using our synthesis method and then segment the tumors based on the completed MR images together with the extracted common features. Moreover, we design a hypernet-based modulation module to adaptively utilize the real and synthetic modalities. Experimental results suggest that our method can not only synthesize reasonable multi-modal MR images, but also achieve state-of-the-art performance on brain tumor segmentation with missing modalities.

Synthesizing a CT image from an available MR image has recently emerged as a key goal in radiotherapy treatment planning for cancer patients. CycleGANs have achieved promising results on unsupervised MR-to-CT image synthesis; however, because they have no direct constraints between input and synthetic images, cycleGANs do not guarantee structural consistency between these two images. This means that anatomical geometry can be shifted in the synthetic CT images, clearly a highly undesirable outcome in the given application. In this paper, we propose a structure-constrained cycleGAN for unsupervised MR-to-CT synthesis by defining an extra structure-consistency loss based on the modality independent neighborhood descriptor. We also utilize a spectral normalization technique to stabilize the training process and a self-attention module to model the long-range spatial dependencies in the synthetic images. Results on unpaired brain and abdomen MR-to-CT image synthesis show that our method produces better synthetic CT images in both accuracy and visual quality as compared to other unsupervised synthesis methods. We also show that an approximate affine pre-registration for unpaired training data can improve synthesis results.