ArticleThe international journal of cardiovascular imaging2024
A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.
Article in The international journal of cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A novel hybrid segmentation method coupled with deep learning for coronary artery extraction from coronary CT angiography.The international journal of cardiovascular imaging · 2026Article
- Atrial volumetrics and recurrence after catheter ablation for atrial fibrillation.Frontiers in cardiovascular medicine · 2026Article
- Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.Scientific reports · 2025Article
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4 authors.
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Abstract
Automatic segmentation of the coronary artery using coronary computed tomography angiography (CCTA) images can facilitate several analyses related to coronary artery disease (CAD). Accurate segmentation of the lumen or plaque region is one of the most important factors. This study aimed to analyze the performance of the coronary artery segmentation of a software platform with a deep learning-based location-adaptive threshold method (DL-LATM) against commercially available software platforms using CCTA. The dataset from intravascular ultrasound (IVUS) of 26 vessel segments from 19 patients was used as the gold standard to evaluate the performance of each software platform. Statistical analyses (Pearson correlation coefficient [PCC], intraclass correlation coefficient [ICC], and Bland-Altman plot) were conducted for the lumen or plaque parameters by comparing the dataset of each software platform with IVUS. The software platform with DL-LATM showed the bias closest to zero for detecting lumen volume (mean difference = -9.1 mm
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