Scholarlink

In this paper, we present an approach for the detection, segmentation, and quantification of stenoses in coronary arteries using modern computer vision and deep learning techniques. Our system incorporates a detection model based on YOLOv8 and a segmentation model (DeepLabV3+) for precise localization and delineation of stenosis regions. In addition, a novel method is introduced to measure arterial thickness to support clinical decision-making. The experimental evaluation shows that the approach demonstrates high quality and performance in comparison to existing solutions. This work aims to improve diagnostic efficiency and reduce the reliance on expensive foreign-made equipment by providing an integrated solution that can operate on standard hardware.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automatic Detection and Segmentation of Coronary Artery Stenosis in Coronary Angiography Images

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Dmitrii Evtyukhov,
  • Georgy Kopanitsa,
  • Oleg Metsker,
  • Aleksandr Mogilevskii,
  • Alexey Yakovlev,
  • Sergey Kovalchuk

摘要

In this paper, we present an approach for the detection, segmentation, and quantification of stenoses in coronary arteries using modern computer vision and deep learning techniques. Our system incorporates a detection model based on YOLOv8 and a segmentation model (DeepLabV3+) for precise localization and delineation of stenosis regions. In addition, a novel method is introduced to measure arterial thickness to support clinical decision-making. The experimental evaluation shows that the approach demonstrates high quality and performance in comparison to existing solutions. This work aims to improve diagnostic efficiency and reduce the reliance on expensive foreign-made equipment by providing an integrated solution that can operate on standard hardware.