Fully automatic four-module deep learning pipeline for identifying, segmenting, and Stanford-subtyping aortic dissection (AD) from CT angiography (CTA). A 3D full-resolution nnU-Net first segments the aorta; the segmented boundary is then multi-view projected for AD identification; for AD-positive cases, a second 3D nnU-Net segments the true lumen (TL) and false lumen (FL); finally, a classifier performs Stanford subtyping from multi-view maximum-density projections of the TL/FL. On 386 CTA scans, the pipeline achieved 0.979 accuracy for AD identification, Dice of 0.968 (TL) and 0.971 (FL) for lumen segmentation, and 0.990 accuracy for Stanford subtyping.

Disentangled representation learning model for cardiac image analysis that factorises 2D medical images (MRI, CT) into a spatial 'anatomy factor' (a semantically meaningful multi-channel map, produced by a U-Net-style anatomy encoder) and a non-spatial 'modality factor' (a latent vector capturing imaging-specific characteristics). This disentangled representation supports semi-supervised segmentation using only a fraction of labeled images (matching fully supervised performance), multi-task learning (e.g. jointly regressing cardiac indices), multimodal pooling of MRI and CT data, and image-to-image synthesis between modalities via latent-space arithmetic (swapping modality factors). SDNet also demonstrates that its modality factor alone can predict the input imaging modality with high accuracy.

Deep channel-attention network for segmenting the full coronary vessel tree from sequential X-ray coronary angiography (XCA) frames, rather than a single static image. An encoder-decoder architecture fuses temporal-spatial feature maps across the XCA sequence via skip connections, then uses channel-attention blocks in the decoder to refine features and separate thin vessel structures from complex, noisy backgrounds; a Dice loss addresses the severe foreground/background class imbalance typical of XCA. The authors report that SVS-net outperforms prior 2D and video-based baselines on both quantitative vessel-segmentation metrics and visual validation.

Deep learning platform for fully automatic segmentation and phenotyping of coronary intravascular ultrasound (IVUS) pullbacks, packaged with a desktop GUI and CLI. A convolutional encoder-decoder network delineates the internal (lumen) and external elastic lamina borders on each cross-sectional IVUS frame; downstream rule-based analysis derives lumen area, plaque area, plaque burden, automatically flags lesions with plaque burden exceeding 40%, and reports minimum lumen area and maximum plaque burden along the pullback. Also supports end-diastolic gating and manual contour editing. Trained on 305 clinical IVUS pullbacks (270 train / 35 validation) from Philips and Boston Scientific catheters at Emory University; downstream evaluations have applied DeepIVUS to tasks such as automated detection of stent underexpansion.

Fully automated deep learning workflow for characterizing cardiac mechanics from balanced steady-state free-precession (bSSFP) cine cardiac MRI. It decouples two convolutional networks—a segmentation net (CarSON) and a 3D motion-estimation net (CarMEN)—to derive left- and right-ventricular volumes plus global and regional myocardial strain and strain rate without manual tracing. Trained and validated on healthy and cardiovascular-disease subjects and shown to be robust across MRI vendors, with excellent intra-scanner repeatability for strain. Developed at Massachusetts General Hospital and the Harvard-MIT Division of Health Sciences and Technology.

Automated pipeline that segments both heart ventricles and tracks their motion throughout the cardiac cycle from short-axis cine cardiac MRI, producing 3D bi-ventricular models with per-vertex wall-thickness and curvature measurements over time. Built on a shape-refined multi-task fully convolutional network, followed by non-rigid registration and mesh-based motion tracking. Trained on roughly 400 manually annotated pulmonary hypertension patients as part of Imperial College London's UK Digital Heart Project, and underlies the related 4Dsurvival cardiac-motion survival-prediction study.

Long-standing toolbox for automated segmentation of the ventricles and atria and derivation of cardiac imaging phenotypes from short- and long-axis cine cardiac MRI. Built on a fully convolutional network trained per slice, and widely reused across UK Biobank cardiac imaging studies since its 2018 publication. Developed at Imperial College London.