ArticleMedical image analysis2020
Atrial scar quantification via multi-scale CNN in the graph-cuts framework.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.Medical image analysis · 2022Pooled it
- SSMSNet: Scribble-Supervised Myocardial Scar Segmentation in Late Gadolinium Enhancement Images.Diagnostics (Basel, Switzerland) · 2026Article
- Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.Journal of imaging · 2026Article
- Poincare guided geometric UNet for left atrial epicardial adipose tissue segmentation in Dixon MRI images.Scientific reports · 2025Article
- Comparison of LGE MRI Scar Identification Methods for Atrial Computational Modeling.Computing in cardiology · 2025Article
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- The value of computed tomography angiography for evaluation of left atrial enlargement in patients with persistent atrial fibrillation.BMC cardiovascular disorders · 2024Article
- FK-means: automatic atrial fibrosis segmentation using fractal-guided K-means clustering with Voronoi-clipping feature extraction of anatomical structures.Interface focus · 2023Article
- Artificial Intelligence in Cardiovascular CT and MR Imaging.Life (Basel, Switzerland) · 2023Review
- Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart.Frontiers in physiology · 2023Article
- Review
- Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network.Journal of biomechanics · 2022Article
- Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond.An international journal on information fusion · 2022Article
- Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity.Entropy (Basel, Switzerland) · 2021Article
- Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.Medical image analysis · 2021Article
- Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives.Frontiers in physiology · 2021Review
- Deep Learning Framework for Real-Time Estimation ofFrontiers in physiology · 2021Article
- Artificial intelligence in cardiac radiology.La Radiologia medica · 2020Review
- Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs.Frontiers in cardiovascular medicine · 2020Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF.
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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.