Dataset Information

Deep learning segmentation of major vessels in X-ray coronary angiography.

ABSTRACT: X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

SUBMITTER: Yang S

PROVIDER: S-EPMC6858336 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Deep learning segmentation of major vessels in X-ray coronary angiography.

Yang Su S Kweon Jihoon J Roh Jae-Hyung JH Lee Jae-Hwan JH Kang Heejun H Park Lae-Jeong LJ Kim Dong Jun DJ Yang Hyeonkyeong H Hur Jaehee J Kang Do-Yoon DY Lee Pil Hyung PH Ahn Jung-Min JM Kang Soo-Jin SJ Park Duk-Woo DW Lee Seung-Whan SW Kim Young-Hak YH Lee Cheol Whan CW Park Seong-Wook SW Park Seung-Jung SJ

Scientific reports 20191115 1

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary ...[more]

PMID: 31729445

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Dataset Information

Deep learning segmentation of major vessels in X-ray coronary angiography.

Publications

Deep learning segmentation of major vessels in X-ray coronary angiography.

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