Zihan Yuan

Myocardial scar and edema segmentation from multi-sequence cardiac magnetic resonance (MS-CMR) is important for myocardial infarction assessment, but remains challenging due to heterogeneous modal characteristics, severe class imbalance, and the small, ambiguous nature of pathological regions. To address these issues, a dynamic synergistic segmentation network (DSS-Net) is proposed for myocardial pathology segmentation. The framework adopts a coarse-to-fine strategy, in which a coarse stage first segments the myocardium to provide anatomical priors and region constraints, and a fine stage then delineates scar and edema within the myocardium-aware space. In addition, a Modality Dynamic Fusion Module (MDFM) is designed to adaptively emphasize pathology-relevant modal information, and a Stage Feature Aggregation Module (SFAM) is introduced to enhance cross-stage feature interactions and fine-grained lesion representation. Experiments on the MyoPS 2020 and MyoPS 2024 datasets demonstrate that DSS-Net achieves competitive and balanced performance, reaching Dice scores of 0.706 for scar and 0.753 for edema on MyoPS 2020. Additionally, compared with SOTA methods in the MyoPS 2020 Challenge, the proposed method attains comparable scar segmentation performance while maintaining a more balanced trade-off between sensitivity and specificity. These findings suggest that combining anatomical guidance with pathology-aware multi-modal learning is a promising strategy for robust myocardial pathology segmentation in MS-CMR images.

Magnetic Resonance Imaging (MRI) is a widely used, non-invasive imaging technology that plays a critical role in clinical diagnostics. Multi-modal MRI, which combines images from different modalities, enhances diagnostic accuracy by offering comprehensive tissue characterization. Meanwhile, multi-modal MRI enhances downstream tasks, like brain tumor segmentation and image reconstruction, by providing richer features. While recent advances in diffusion models (DMs) show potential for high-quality image translation, existing methods still struggle to preserve fine structural details and ensure accurate image synthesis in medical imaging. To address these challenges, we propose a Frequency-Aware Diffusion Model (FADM) for generating high-quality target modality MRI images from source modality images. The FADM incorporates a discrete wavelet transform within the diffusion model framework to extract both low- and high-frequency information from MRI images, enhancing the capture of tissue structural and textural features. Additionally, a wavelet downsampling layer and supervision module are incorporated to improve frequency awareness and optimize high-frequency detail extraction. Experimental results on the BraTS 2021 dataset and a 1.5T–3T MRI dataset demonstrate that the FADM outperforms existing generative models, particularly in preserving intricate brain structures and tumor regions while generating high-quality MRI images.