ArticleScientific data2024
HVSMR-2.0: A 3D cardiovascular MR dataset for whole-heart segmentation in congenital heart disease.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Contrastive language image pretraining for a cardiac magnetic resonance image embedding with zero-shot capabilities.Nature communications · 2026Article
- Artificial intelligence, extended reality and computational modelling in cross-sectional cardiovascular imaging in congenital heart disease: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Dataset for studying deformation in 3D patient-specific pulmonary artery anatomies.Scientific data · 2026Article
- A novel hybrid layer-based encoder-decoder framework for 3D segmentation in congenital heart disease.Scientific reports · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Greater than the sum of its parts: multimodality imaging in adults with congenital heart disease.Cardiovascular diagnosis and therapy · 2024Review
- HVSMR-2.0: A 3D cardiovascular MR dataset for whole-heart segmentation in congenital heart disease.Scientific data · 2024Article
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Authors and funding
14 authors.
Funding
Abstract
Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image segmentation from a preoperative cardiovascular magnetic resonance (CMR) scan would enable creation of patient-specific 3D surface models of the heart, which have potential to improve surgical planning, enable surgical simulation, and allow automatic computation of quantitative metrics of heart function. However, there is no publicly available CMR dataset for whole-heart segmentation in patients with congenital heart disease. Here, we release the HVSMR-2.0 dataset, comprising 60 CMR scans alongside manual segmentation masks of the 4 cardiac chambers and 4 great vessels. The images showcase a wide range of heart defects and prior surgical interventions. The dataset also includes masks of required and optional extents of the great vessels, enabling fairer comparisons across algorithms. Detailed diagnoses for each subject are also provided. By releasing HVSMR-2.0, we aim to encourage development of robust segmentation algorithms and clinically relevant tools for congenital heart disease.
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