ArticleIEEE transactions on medical imaging2019
Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH).Medicina (Kaunas, Lithuania) · 2025Pooled it
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- Retinal layer segmentation in OCT images with a 2.5D cross-slice feature fusion module for glaucoma assessment.Biomedical optics express · 2026Article
- Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale.Scientific reports · 2026Article
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- 2D, 2.5D, or 3D? Comparing Dimensional Approaches in Deep Neural Networks for 3D Medical Image Analysis.Journal of imaging informatics in medicine · 2026Review
- Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.Pulmonary circulation · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 2024Review
- Attention-guided context asymmetric fusion networks for the liver tumor segmentation of computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- PELE scores: pelvic X-ray landmark detection with pelvis extraction and enhancement.International journal of computer assisted radiology and surgery · 2024Article
- DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning.IEEE transactions on medical imaging · 2024Article
- LAVASET: Latent Variable Stochastic Ensemble of Trees. An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies.Bioinformatics (Oxford, England) · 2024Article
- Liver shape analysis using statistical parametric maps at population scale.BMC medical imaging · 2024Article
- A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.Radiology. Artificial intelligence · 2024Article
- Interactive segmentation with curve-based template deformation for spatiotemporal computed tomography of swallowing motion.PloS one · 2024Article
- Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.Circulation. Genomic and precision medicine · 2023Article
- A task-unified network with transformer and spatial-temporal convolution for left ventricular quantification.Scientific reports · 2023Article
- Cardiac Magnetic Resonance Imaging (CMRI) Applications in Patients with Chest Pain in the Emergency Department: A Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
10 authors.
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Abstract
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes.
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