ArticleMedical physics2020
Cardiac substructure segmentation with deep learning for improved cardiac sparing.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- A neighborhood attention transformer network for enhanced 3D segmentation of the left anterior descending artery.Medical physics · 2026Article
- Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning.Physics and imaging in radiation oncology · 2026Article
- Automated extraction of the plane of minimal hiatal dimensions and mid-sagittal plane from 3D transperineal ultrasound.Medical physics · 2026Article
- Deducing cardiorespiratory motion of cardiac substructures using a novel 5D-MRI workflow for radiotherapy.Physics in medicine and biology · 2026Article
- Radiation Doses to Cardiac Substructures Predict Elevation in High-sensitivity Cardiac Troponin T Levels in Radiation Therapy for Lung Cancer.International journal of radiation oncology, biology, physics · 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
- Dosimetric Performance of Orthogonal Dual-Layer Multi-Leaf Collimator System on Locally Advanced Lung Cancer: Cardiac Substructures Sparing Plans.Advances in radiation oncology · 2025Article
- Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy.Advances in radiation oncology · 2025Article
- Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation.ArXiv · 2025Article
- Automated contouring for breast cancer radiotherapy in the isocentric lateral decubitus position: a neural network-based solution for enhanced precision and efficiency.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [EJNMMI physics · 2025Article
- Pilot study to assess the early cardiac safety of carbon ion radiotherapy for intra- and para-cardiac tumours.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2024Article
- Cardiac substructure delineation in radiation therapy - A state-of-the-art review.Journal of medical imaging and radiation oncology · 2024Review
- Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning.International journal of radiation oncology, biology, physics · 2024Article
- Deep learning-based automatic segmentation of cardiac substructures for lung cancers.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024Article
- Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!BJR artificial intelligence · 2024Review
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Anatomical Prior-Based Automatic Segmentation for Cardiac Substructures from Computed Tomography Images.Bioengineering (Basel, Switzerland) · 2023Article
- A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy.Physics and imaging in radiation oncology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeRadiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI's soft tissue contrast coupled with CT for state-of-the-art cardiac substructure segmentation requiring a single, non-contrast CT input. MATERIALS/
methodsThirty-two left-sided whole-breast cancer patients underwent cardiac T2 MRI and CT-simulation. A rigid cardiac-confined MR/CT registration enabled ground truth delineations of 12 substructures (chambers, great vessels (GVs), coronary arteries (CAs), etc.). Paired MRI/CT data (25 patients) were placed into separate image channels to train a three-dimensional (3D) neural network using the entire 3D image. Deep supervision and a Dice-weighted multi-class loss function were applied. Results were assessed pre/post augmentation and post-processing (3D conditional random field (CRF)). Results for 11 test CTs (seven unique patients) were compared to ground truth and a multi-atlas method (MA) via Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Wilcoxon signed-ranks tests. Three physicians evaluated clinical acceptance via consensus scoring (5-point scale).
resultsThe model stabilized in ~19 h (200 epochs, training error <0.001). Augmentation and CRF increased DSC 5.0 ± 7.9% and 1.2 ± 2.5%, across substructures, respectively. DL provided accurate segmentations for chambers (DSC = 0.88 ± 0.03), GVs (DSC = 0.85 ± 0.03), and pulmonary veins (DSC = 0.77 ± 0.04). Combined DSC for CAs was 0.50 ± 0.14. MDA across substructures was <2.0 mm (GV MDA = 1.24 ± 0.31 mm). No substructures had statistical volume differences (P > 0.05) to ground truth. In four cases, DL yielded left main CA contours, whereas MA segmentation failed, and provided improved consensus scores in 44/60 comparisons to MA. DL provided clinically acceptable segmentations for all graded patients for 3/4 chambers. DL contour generation took ~14 s per patient.
conclusionsThese promising results suggest DL poses major efficiency and accuracy gains for cardiac substructure segmentation offering high potential for rapid implementation into RTP for improved cardiac sparing.
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Registered trials
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.