Szczegóły publikacji
Opis bibliograficzny
A novel framework for differentiating vessel-like objects in coronarography images / Witold Serwatka, Katarzyna HERYAN, Joanna SORYSZ, Marcin Jarzab, Kamil Sterna // W: EMBC 2023 [Dokument elektroniczny] : 2023 45th annual international conference of the IEEE Engineering in Medicine & Biology Conference : Sydney, Australia, 24-27 July 2023 : proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway, NJ, USA] : IEEE, cop. 2023. — (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X). — Dod. ISBN 979-8-3503-2448-8 (PoD). — e-ISBN: 979-8-3503-2447-1. — S. [1–4]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [4], Abstr. — W. Serwatka, J. Sorysz – dod. afiliacja: Autosymed SRL, Iasi, Romania
Autorzy (5)
- AGHSerwatka Witold
- AGHHeryan Katarzyna
- AGHSorysz Joanna
- Jarzab M.
- Sterna K.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 152434 |
|---|---|
| Data dodania do BaDAP | 2024-03-19 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC40787.2023.10341105 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | The Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
Coronary Artery Disease is the leading cause of death worldwide. Its prevalence will grow while access to specialized medical care will be further limited due to staff shortages. Therefore, any facilitation of diagnosis or treatment is of paramount importance. The diagnosis based on Coronary Angiography can be automated to perform a quantitative evaluation of lesions. This requires precise segmentation of coronary arteries. At the moment, the state-of-the-art algorithms fail to eliminate vessel-like artifacts that are wrongly included in segmentation results (e.g. catheters, stitches). This is a bottleneck for the automatization of the diagnosis workflow that precedes clinical action. In this paper, we propose a 2-step post-segmentation refinement algorithm. A binary segmentation of the coronary arteries is used to extract image features - inputs for an XGBoost Classifier. Its predictions are improved by a neighborhood filter that leverages contextual information to assign correct labels. The algorithm is primarily concerned with differentiating vessels from other vessel-like objects and does so with a 99% accuracy rate. It takes advantage of an original local description of Tamura features, which proved to be one of the most influential factors in decision-making. As a result, the segmentation of coronary arteries is cleaned from artifacts, enabling AI-supported diagnosis workflows to be automated. After re-training, the proposed method can be used to eliminate post-segmentation artifacts in other medical domains.Clinical relevance - The algorithm proposed in this paper allows for the development of software that could automatically calculate the Syntax Score in real time. This would shorten diagnostics time and allow for immediate action in critical cases.
