CVAI Catalog

·

View Catalog

4Dsegment (bi-ventricular segmentation + motion tracking)

Imperial College London (Duan et al., UK Digital Heart Project)

Cardiac MRI

Filter catalog by Modality:
Cardiac MRI

Cardiac chamber segmentation

Filter catalog by Disease / Trait:
Structural Heart & Cardiomyopathy

Segmentation

Filter catalog by Task Type:
Segmentation & Detection

CNN (2D)

Filter catalog by Architecture:
Convolutional (CNN)

TensorFlow

Filter catalog by Framework:
TensorFlow / Keras

GPL 3.0

Filter catalog by License:
Copyleft

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.

memory Specifications

category

Architecture

CNN (2D)

Shape-refined multi-task fully convolutional network for bi-ventricular segmentation, followed by non-rigid co-registration and mesh-based motion tracking

code

Framework

TensorFlow

calendar_month

Added to catalog

2026-07-10

gavel License

GPL 3.0

check_small Open source check_small Commercial use OK check_small Modifications allowed Attribution required Share-alike required

License for model weights only. Associated code may be licensed seperately, check code source for specific terms.

description Publication

database Training & evaluation data

UK Digital Heart Project (pulmonary hypertension CMR cohort)

train

public 400 subjects · United Kingdom

Manually annotated CMR studies from pulmonary hypertension patients; used to train 4Dsegment

science Capabilities & performance

Segmentation

Cardiac chamber segmentation