ArticleComputer methods in biomechanics and biomedical engineering. Imaging & visualization2019
Integrated 3D Anatomical Model for Automatic Myocardial Segmentation in Cardiac CT Imagery.
Article in Computer methods in biomechanics and biomedical engineering. Imaging & visualization, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
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
5 citing papers in PubMed, 14 citations in OpenAlex.
- Accuracy of individualized 3D modeling of ossicles using high-resolution computed tomography imaging data.Quantitative imaging in medicine and surgery · 2021Article
- Deep learning approach for the segmentation of aneurysmal ascending aorta.Biomedical engineering letters · 2021Article
- Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging.Applied sciences (Basel, Switzerland) · 2021Article
- Dependently Coupled Principal Component Analysis for Bivariate Inversion Problems.Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition · 2021Article
- Rationale and design of the quantification of myocardial blood flow using dynamic PET/CTA-fused imagery (DEMYSTIFY) to determine physiological significance of specific coronary lesions.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Segmentation of epicardial and endocardial boundaries is a critical step in diagnosing cardiovascular function in heart patients. The manual tracing of organ contours in Computed Tomography Angiography (CTA) slices is subjective, time-consuming and impractical in clinical setting. We propose a novel multi-dimensional automatic edge detection algorithm based on shape priors and principal component analysis (PCA). We have developed a highly customized parametric model for implicit representations of segmenting curves (3D) for Left Ventricle (LV), Right Ventricle (RV), and Epicardium (Epi) used simultaneously to achieve myocardial segmentation. We have combined these representations in a region-based image modeling framework with high level constraints enabling the modeling of complex cardiac anatomical structures to automatically guide the segmentation of endo/epicardial boundaries. Test results on 30 short-axis CTA datasets show robust segmentation with error (mean ± std mm) of (1.46 ± 0.41), (2.06 ± 0.65), (2.88 ± 0.59) for LV, RV and Epi respectively.
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What OpenQuestion holds
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.