| Autor: |
Maxim Popov, Akmaral Amanturdieva, Nuren Zhaksylyk, Alsabir Alkanov, Adilbek Saniyazbekov, Temirgali Aimyshev, Eldar Ismailov, Ablay Bulegenov, Arystan Kuzhukeyev, Aizhan Kulanbayeva, Almat Kalzhanov, Nurzhan Temenov, Alexey Kolesnikov, Orazbek Sakhov, Siamac Fazli |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Data, Vol 11, Iss 1, Pp 1-9 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2052-4463 |
| DOI: |
10.1038/s41597-023-02871-z |
| Popis: |
Abstract X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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