Cross-modality cardiac image segmentation model that addresses spatial-temporal confounding -- where the anatomy and imaging-modality elements of cardiac images are intertwined across space and time. DCL performs multi-dimensional causal intervention, modeling causal relationships between images and labels as well as causality along the time and space dimensions, integrating historical optimal interventions to transfer knowledge across temporal contexts. A diffusion mechanism further keeps extracted anatomical elements causally invariant across modalities. On cross-modality cardiac images (MR, CT, and ultrasound), DCL achieved a mean Dice of 0.951, outperforming other advanced segmentation methods.

Machine-learning surrogate model for estimating pulsatile hemodynamic fields (velocity, pressure) in coronary arteries from a steady-state computational fluid dynamics (CFD) prior, avoiding the high computational cost of full pulsatile CFD. The model, a neural field conditioned on hemodynamic boundary conditions, is discretisation-independent and can be parametrised with message-passing or self-attention layers by relaxing point-wise action to permutation-equivariance. Evaluated on 74 stenotic coronary arteries from coronary CT angiography (CCTA) with patient-specific pulsatile CFD as ground truth, the model produced accurate, discretisation-independent estimates of pulsatile velocity and pressure fields.

Automatic coronary artery segmentation pipeline for coronary CT angiography (CCTA). A 2D DenseNet classifier first screens out CT slices that don't contain coronary artery, then a 3D-UNet -- enhanced with dense blocks in the encoder for richer feature extraction and residual, feature-rectifying blocks in the decoder -- segments the coronary artery tree in the remaining slices. A Gaussian-weighted merging scheme combines overlapping 3D patch predictions, up-weighting the more reliable predictions near each patch's center. On the authors' in-house CCTA dataset, the method achieved a Dice similarity coefficient of 0.826.

Deep learning model for multi-class 3D segmentation of the aorta and its thirteen branches from CT angiography, intended to support planning of endovascular aortic interventions. CIS-UNet combines a CNN encoder with a symmetric decoder and a novel Context-aware Shifted Window Self-Attention (CSW-SA) bottleneck block that adapts the Swin transformer's patch-merging mechanism to more efficiently capture global spatial context. Trained and evaluated via 4-fold cross-validation on the first public multi-branch aorta CTA dataset (59 patients), CIS-UNet outperformed the state-of-the-art SwinUNETR baseline, achieving a mean Dice of 0.713 vs. 0.697 and mean surface distance of 2.78mm vs. 3.39mm, while being more computationally efficient.

Coronary artery calcium (CAC) scoring model that transfers a CNN trained for calcium scoring on non-contrast CT (NCCT) to coronary CT angiography (CCTA), where iodinated contrast otherwise confounds calcium detection and large annotated CCTA training sets are scarce. The CAC-scoring CNN is split into a feature generator and a classifier; the feature generator is trained on the NCCT source domain and adapted to the CCTA target domain via adversarial learning combined with a maximum-mean-discrepancy loss, while the source-domain classifier is reused unchanged for the target domain. Builds directly on the authors' earlier non-contrast CT calcium-scoring network.

End-to-end deep learning framework that predicts the procedural outcome of percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) lesions directly from preprocedural coronary CT angiography, aiming to replace slower manual scoring systems (J-CTO, CT-RECTOR, KCCT). The pipeline first segments the coronary artery tree (Patch-UCTNet), detects candidate CTO lesions along the delineated vessel, extracts pathological lesion features with a Swin Transformer, and classifies two outcomes: successful guidewire crossing within 30 minutes and overall PCI success. In the original study, the model completed reconstruction and analysis 85% faster than manual scores (73.7s vs. 418-467s) and was more accurate than the manual CT-RECTOR, KCCT, and J-CTO_CCTA_ scores, reaching an AUROC of 0.97 on the internal test set and 0.96 on an independent external validation cohort (186 patients, 189 CTO lesions).

Multi-instance-learning (MIL) model for detecting >=50% coronary stenosis directly from curved multiplanar reformation (CMR) images generated during routine coronary CT angiography (CCTA) reads, without requiring slice-level annotations. A multi-range Hounsfield-unit preprocessing pipeline (Sobel edge detection across five attenuation windows) highlights plaque and vessel-wall structures, which a VGG16-based encoder with positional encoding and multi-head attention aggregates across each patient's 'bag' of up to 36 CMR slices per artery to give an interpretable, attention-weighted patient-level prediction. Trained and five-fold cross-validated on 900 real-world CCTA cases (776 LAD / 694 RCA / 600 LCX) from Sahlgrenska University Hospital, reaching AUCs of 0.91-0.92 across the three major coronary arteries. Code (preprocessing + MIL training pipeline) is public; the clinical CMR dataset and trained weights are not released.