The synthesis of computed tomography images can supplement electron density information and eliminate MR-CT image registration errors. Consequently, an increasing number of MR-to-CT image translation approaches are being proposed for MR-only radiotherapy planning. However, due to substantial anatomical differences between various regions, traditional approaches often require each model to undergo independent development and use. In this paper, we propose a unified model driven by prompts that dynamically adapt to the different anatomical regions and generates CT images with high structural consistency. Specifically, it utilizes a region-specific attention mechanism, including a region-aware vector and a dynamic gating factor, to achieve MRI-to-CT image translation for multiple anatomical regions. Qualitative and quantitative results on three datasets of anatomical parts demonstrate that our models generate clearer and more anatomically detailed CT images than other state-of-the-art translation models. The results of the dosimetric analysis also indicate that our proposed model generates images with dose distributions more closely aligned to those of the real CT images. Thus, the proposed model demonstrates promising potential for enabling MR-only radiotherapy across multiple anatomical regions. we have released the source code for our RSAM model. The repository is accessible to the public at: https://github.com/yhyumi123/RSAM.

Modelling disease progression in precision medicine requires capturing complex spatio-temporal dynamics while preserving anatomical integrity. Existing methods often struggle with longitudinal dependencies and structural consistency in progressive disorders. To address these limitations, we introduce MambaControl, a novel framework that integrates selective state-space modelling with diffusion processes for high-fidelity prediction of medical image trajectories. To better capture subtle structural changes over time while maintaining anatomical consistency, MambaControl combines Mamba-Based long-range modelling with graph- guided anatomical control to more effectively represent anatomical correlations. Furthermore, we introduce Fourier-Enhanced spectral graph representations to capture spatial coherence and multiscale detail, enabling MambaControl to achieve state-of-the-art performance in Alzheimer’s disease prediction. Quantitative and regional evaluations demonstrate improved progression prediction quality and anatomical fidelity, highlighting its potential for personalised prognosis and clinical decision support.

Accurate segmentation of blood vessels is essential for various clinical assessments and postoperative analyses. However, the inherent challenges of vascular imaging-such as sparsity, fine granularity, low contrast, data distribution variability, and the critical need for preserving topological integrity-make generalized vessel segmentation particularly complex. While specialized segmentation methods have been developed for specific anatomical regions, their over-reliance on tailored models hinders broader applicability and generalization. General-purpose segmentation models introduced in medical imaging often fail to address critical vascular characteristics, including the connectivity of segmentation results. In this study, we propose OVS-Net, an optimized vessel segmentation framework designed to generalize across diverse vessel structures and imaging modalities. It introduces a dual-branch architecture design for improving small vessel segmentation and a morphology-aware correction module to preserve vascular topology and connectivity. We compiled a comprehensive multi-modality dataset from 17 datasets to train and benchmark the proposed OVS-Net against 6 SAM-based methods and 17 expert models under various conditions. The results demonstrate that our approach achieves superior segmentation accuracy, generalization, and a 34.6% improvement in connectivity, underscoring its potential for clinical applications. The code and dataset information are available at https://github.com/Hk416mod2/OVS-Net.

Vision foundation models have shown great potential in improving generalizability and data efficiency, especially for medical image segmentation since medical image datasets are relatively small due to high annotation costs and privacy concerns. However, current research on foundation models predominantly relies on transformers. The high quadratic complexity and large parameter counts make these models computationally expensive, limiting their potential for clinical applications. In this work, we introduce Swin-UMamba†, a novel Mamba-based model for medical image segmentation that seamlessly leverages the power of the vision foundation model, which is also computationally efficient with the linear complexity of Mamba. Moreover, we investigated and verified the impact of the vision foundation model on medical image segmentation, in which a self-supervised model adaptation scheme was designed to bridge the gap between natural and medical data. Notably, Swin-UMamba† outperforms 7 state-of-the-art methods, including CNN-based, transformer-based, and Mamba-based approaches across AbdomenMRI, Encoscopy, and Microscopy datasets. The code and models are publicly available at: https://github.com/JiarunLiu/Swin-UMamba.

Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registration. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to improve the robustness and achieve end-to-end registration. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset containing 555 cases, and encouraging performances have been achieved. Compared to the commonly utilized registration methods, including VoxelMorph, SyN, and LT-Net, the proposed method achieves better registration performance with a Dice score of 0.8397± 0.0756 in identifying stroke lesions. With regards to the registration speed, our method is about 10 times faster than the most competitive method of SyN (Affine) when testing on a CPU. Moreover, we prove that our method can still perform well on more challenging tasks with lacking scanning information data, showing the high robustness for the clinical application.