Vascular segmentation is a critical task in clinical medical image processing and a prerequisite for accurately diagnosing vascular-related diseases. The development of automated segmentation methods is challenged by internal variability in vessel representations. Recently, topology guidance has shown potential for capturing semantically consistent representations. However, current topology-guided methods lack modeling of global-to-local dependencies. This limitation forces latent representations subject to a trade-off between learning global topology and local geometries within the vascular network. In this paper, we propose a Bayesian-based topology-guided (BayeTopo) learning approach to capture global-to-local dependencies. It introduces a prior that explicitly models local geometry as a probability conditioned on global topology within topology-sensitive regions of the vascular network. We further implement a topology-guided diffusion model to optimize the conditional probability. It gradually infers local geometry from the restored global topology with multi-scale noise, enabling rich global-to-local representations. Then, an inhomogeneous diffusion process is involved, where noise initially accumulates in topology-sensitive regions before achieving uniformity. It ensures an orderly degradation of information from global topology to local geometry, thereby enabling effective global-to-local supervision. Extensive experiments on six datasets, involving three types of vascular networks under four imaging modalities, demonstrate the superior performance and generalization capability of our method compared to previous topology-guided learning and diffusion-based models. A series of case studies further validates the effectiveness of our designs in enhancing semantic consistency within local vascular regions, thereby improving topological accuracy.

Contrast-free myocardial infarction (MI) segmentation is essential for mitigating the health risks associated with contrast agents (CAs) in clinical diagnostics. However, existing approaches are limited by their reliance on strictly paired CINE sequences and contrast-enhanced images, which are often difficult to obtain because patient conditions and imaging protocols often cause inter-modality slice misalignments. Therefore, we propose MTMS, the first label-free training and contrast-free MI segmentation model, enabling effective training without requiring paired datasets. Notably, MTMS is the first framework to incorporate cardiac biomechanical knowledge into contrast-free MI segmentation through a self-supervised paradigm. It leverages dual upstream guidance, combining pseudo-label generation from biomechanical cues with structural for segmentation, and achieves self-supervised learning via iterative pseudo-label refinement. MTMS includes three synergistic modules, Upstream 1: Spatiotemporal Structural Evolution Module that encodes myocardial structure transitions by guided-perturbation modeling of inter-frame morphological divergence, enabling explicit extraction of deformation trajectories critical for infarct localization; Upstream 2: Cardiac Mechanics-Driven Analysis Module that estimates myocardial stress responses by diffeomorphic motion fields and strain energy formulation, enabling generation of physiologically consistent pseudo-labels that reflect regional mechanical dysfunction; Downstream: Dual-Domain Interaction Module that combines structural and biomechanical cues by prototype-guided semantic fusion, enabling consistent and physiologically grounded delineation of infarct boundaries. On 370 clinical cases, MTMS achieves a Dice of 0.698 and HD95 of 19.634, surpassing seven state-of-the-art methods by up to 0.30 in Dice and over 107.392 in HD95. These results demonstrate the potential of MTMS to advance the development of contrast-free MI segmentation. Code is available at https://github.com/wrsssss/mtms.

Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality. Unfortunately, algorithms for cardiac data synthesis have been so far scarcely studied in the literature. An important imaging modality in the cardiac examination is three-directional CINE multi-slice myocardial velocity mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion in three orthogonal directions of the left ventricle. The long acquisition time and complex acquisition produce make it more urgent to produce synthetic digital twins of this imaging modality. In this study, we propose a hybrid deep learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm is featured by a hybrid UNet and a Generative Adversarial Network with a foreground-background generation scheme. The experimental results show that from temporally down-sampled magnitude CINE images (six times), our proposed algorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92). These performance scores indicate that our proposed HDL algorithm can be implemented in real-world digital twins for myocardial velocity mapping data simulation. To the best of our knowledge, this work is the first one investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data.

Iodinated contrast medium (ICM) dose reduction is beneficial for decreasing potential health risk to renal-insufficiency patients in CT scanning. Due to the low-intensity vessel in ultra-low-dose-ICM CT angiography, it cannot provide clinical diagnosis of vascular diseases. Angiography reconstruction for ultra-low-dose-ICM CT can enhance vascular intensity for directly vascular diseases diagnosis. However, the angiography reconstruction is challenging since patient individual differences and vascular disease diversity. In this paper, we propose a Multiple Adversarial Learning based Angiography Reconstruction (i.e., MALAR) framework to enhance vascular intensity. Specifically, a bilateral learning mechanism is developed for mapping a relationship between source and target domains rather than the image-to-image mapping. Then, a dual correlation constraint is introduced to characterize both distribution uniformity from across-domain features and sample inconsistency within domain simultaneously. Finally, an adaptive fusion module by combining multi-scale information and long-range interactive dependency is explored to alleviate the interference of high-noise metal. Experiments are performed on CT sequences with different ICM doses. Quantitative results based on multiple metrics demonstrate the effectiveness of our MALAR on angiography reconstruction. Qualitative assessments by radiographers confirm the potential of our MALAR for the clinical diagnosis of vascular diseases.

Cross-modality data processing is critical for the Internet-of-Things (IoT) deployment in healthcare. It can convert the innumerable raw day-to-day medical big data from massive IoT-based medical devices to diagnostic valuable data so that they can be feed to clinical routine. In this article, we propose a novel spatiotemporal two-streams generative adversarial network (SpGAN) as a cross-modality data processing approach to deploy the medical IoT in infarction learning. Our SpGAN remotely converts diagnostic valuable contrast-enhanced images (the “gold standard” for infarction learning, but it requires the injection of contrast agents) directly from raw nonenhanced cine MR images. This converting allows physicians to remotely perform infarction observation and analysis to break through the limitations of time and space by building a cloud computing platform of IoT-based MRI devices. Importantly, this converting offers a low-risk IoT-based manner to eliminate the potential fatal risk caused by contrast agent injection in the current infarction learning workflow. Specifically, SpGAN consists of: 1) a spatiotemporal two-stream framework as an encoding–decoding model to achieve data converting and 2) a spatiotemporal pyramid network enhances those features that are responsible to the infarction learning during encoding to improve decoding performance. Real IoT-based remote diagnosis experiments performed on 230 patients demonstrate that SpGAN provides high-quality converted images for infarction learning and promotes the in-depth application and deployment of IoT in the medical field.

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (∼0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60–68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.