ArticleKorean journal of radiology2021
A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection.
Article in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Modernizing Aortic Dissection Classification in the Era of Endovascular and Hybrid Repair: Implications for Radiology Reporting.Korean journal of radiology · 2026Review
- Deep learning-driven false-lumen volumes predict adverse remodeling better than diameter in patients with residual aortic dissection on CT.European radiology · 2026Article
- Performance of image-based deep learning models for aortic dissection segmentation and diagnosis: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
- Automatic Diagnosis, Classification, and Segmentation of Abdominal Aortic Aneurysm and Dissection from Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2025Article
- A Fully Automatic Pipeline of Identification, Segmentation, and Subtyping of Aortic Dissection from CT Angiography.Cardiovascular engineering and technology · 2025Article
- Mechanisms of aortic dissection: From pathological changes to experimental andProgress in materials science · 2025Article
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluation of the clinical application value of artificial intelligence in diagnosing head and neck aneurysms.BMC medical imaging · 2024Article
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- Dual-layer spectral computed tomography aortography using a seventy-five-percent-reduced iodine dose protocol and multiparameter spectral imaging: comparison with conventional computed tomography imaging.Quantitative imaging in medicine and surgery · 2023Article
- Emerging role of radiogenomics in genetically triggered thoracic aortic aneurysm and dissection: a narrative review.Annals of translational medicine · 2023Review
- Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.European radiology experimental · 2023Article
- Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis.Diagnostics (Basel, Switzerland) · 2022Review
- Machine learning in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2022Review
- Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.European radiology · 2022Article
- Application of Artificial Intelligence to Cardiovascular Computed Tomography.Korean journal of radiology · 2021Review
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Authors and funding
8 authors.
Funding
Abstract
objectiveTo provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). MATERIALS AND
methodsAortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated.
resultsThe mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (
conclusionThe performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.
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