ArticleMedical image analysis2019
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 3 of them syntheses that pooled it.
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Who cites it
77 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning for cardiac CT segmentation for congenital heart disease: A systematic review.PloS one · 2026Pooled it
- Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.Medical & biological engineering & computing · 2025Pooled it
- Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.Medical image analysis · 2022Pooled it
- Consensus approach to shape regularization in cross-modality cardiac segmentation.Radiological physics and technology · 2026Article
- VentrEX: An Anatomically Guided Deep Learning Pipeline for Ventricular Segmentation in Cine Cardiac MRI.Journal of imaging · 2026Article
- Echo Chambers: Bias and Representation in Cardiac Imaging Datasets for Artificial Intelligence.Current cardiology reports · 2026Review
- Deformable multi-head cross attention-based 3D U-net for context-aware multi-modal cardiac image segmentation with uncertainty-aware maps.Scientific reports · 2026Article
- Three-dimensional visualization of arrhythmogenic substrate in mouse hearts using panoramic optical mapping and micro-computed tomography.Nature cardiovascular research · 2026Article
- Innovations In Artificial Intelligence-Guided Atherosclerosis Cardiovascular Imaging: From Detection to Prognosis.Current atherosclerosis reports · 2026Review
- A Systematic Review of Deep Learning Approaches for Hepatopancreatic Tumor Segmentation.Journal of imaging · 2026Review
- Progression-Aware and Explainable CNN-Transformer Framework for Multiclass Alzheimer's Disease Staging Using MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Modality-AGnostic image Cascade (MAGIC) for multi-modality cardiac substructure segmentation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
- Adaptive composite loss for volumetric whole heart segmentation.Scientific reports · 2025Article
- DG-TTA: Out-of-Domain Medical Image Segmentation Through Augmentation, Descriptor-Driven Domain Generalization, and Test-Time Adaptation.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy.Advances in radiation oncology · 2025Article
- Refining cardiac segmentation from MRI volumes with CT labels for fine anatomy of the ascending aorta.Radiological physics and technology · 2025Article
- Cascaded Self-Supervision to Advance Cardiac MRI Segmentation in Low-Data Regimes.Bioengineering (Basel, Switzerland) · 2025Article
- Masked autoencoders with generalizable self-distillation for skin lesion segmentation.Medical & biological engineering & computing · 2025Review
- Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation.ArXiv · 2025Article
17 more citing papers are in PubMed but not listed here.
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34 authors.
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Abstract
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.