Children's Hospital of Philadelphia / MIT / Queen's University (Herz, Jolley et al.)
Model weights not public. Contact creators for more information.
Deep learning framework, developed in collaboration with the MONAI community, for automatic segmentation of tricuspid valve leaflets from transthoracic 3D echocardiograms in children with hypoplastic left heart syndrome (HLHS) and other forms of single-ventricle congenital heart disease, integrated into 3D Slicer via MONAILabel for interactive clinical/research use. Addresses a modality (pediatric 3D echocardiography) and population (single-ventricle congenital heart disease) largely absent from adult-focused cardiac AI models.
Architecture
CNN (3D)
Fully convolutional 3D network (MONAI-based) for multi-label tricuspid-valve-leaflet segmentation from 3D transthoracic echocardiography volumes, deployed interactively via MONAILabel/3D Slicer
Framework
PyTorch
Added to catalog
2026-08-10
CHOP Pediatric HLHS 3D Echocardiography Cohort
161 transthoracic 3D echocardiogram (3DE) images from 129 unique pediatric patients with hypoplastic left heart syndrome (HLHS) at Children's Hospital of Philadelphia, with manual tricuspid valve leaflet and annulus segmentations.
Multi-label tricuspid valve leaflet (anterior/posterior/septal) segmentation from 3D transthoracic echocardiography in pediatric hypoplastic left heart syndrome