ArticlePloS one2020
Cardiac MR segmentation based on sequence propagation by deep learning.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Left ventricular segmentation method based on optimized UNet and improved CBAM: ESV and EDV tracking study.PloS one · 2025Article
- Combining generative modelling and semi-supervised domain adaptation for whole heart cardiovascular magnetic resonance angiography segmentation.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Article
- Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension.European heart journal. Digital health · 2022Article
- Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
4 authors.
Funding
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Abstract
Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it extremely difficult to accurately and quickly manually segment the myocardial. In this paper, we propose a method for CMR segmentation based on U-Net and combined with image sequence information. The method can effectively segment from the top slice to the bottom slice of the CMR. During training, each input slice depends on the slice below it. In other words, the predicted segmentation result depends on the existing segmentation label of the previous slice. 3D sequence information is fully utilized. Our method was validated on the ACDC dataset, which included CMR images of 100 patients (1700 2D MRI). Experimental results show that our method can segment the myocardial quickly and efficiently and is better than the current state-of-the-art methods. When evaluating 340 CMR image, our model yielded an average dice score of 85.02 ± 0.15, which is much higher than the existing classical segmentation method(Unet, Dice score = 0.78 ± 0.3).
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