基于黑森深度学习的冠状动脉造影图像分析预处理方法

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yanjun Li, Takaaki Yoshimura, Yuto Horima, Hiroyuki Sugimori
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引用次数: 0

摘要

基于深度学习的冠状动脉检测技术具有高准确性和稳定性,已被广泛应用于冠状动脉疾病的诊断。然而,由于成像角度和造影剂分布不均等因素,传统的冠状动脉狭窄定位算法在检测分支血管狭窄时往往存在不足,这可能会对健康造成重大风险。为了应对这些挑战,我们提出了一种预处理方法,将基于黑森的血管增强和图像融合作为深度学习的先决条件。这种方法增强了冠状动脉造影图像中的模糊特征,从而提高了神经网络对血管狭窄特征的敏感性。我们使用最新的深度学习网络(如 YOLOv10、YOLOv9 和 RT-DETR)评估了该方法在各种评价指标上的有效性。结果表明,与未经特殊预处理的图像相比,我们的方法在 RT-DETR R101 和 YOLOv10-X 上将 AP50 的准确率分别提高了 4.84% 和 5.07%。此外,我们还分析了不同成像角度对狭窄定位检测的影响,结果表明左冠状动脉零点最适合检测狭窄,AP50(%) 值为 90.5。实验结果表明,所提出的方法作为基于深度学习的冠状动脉造影图像处理的预处理技术是有效的,并增强了模型识别小血管狭窄的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hessian-Based Deep Learning Preprocessing Method for Coronary Angiography Image Analysis
Leveraging its high accuracy and stability, deep-learning-based coronary artery detection technology has been extensively utilized in diagnosing coronary artery diseases. However, traditional algorithms for localizing coronary stenosis often fall short when detecting stenosis in branch vessels, which can pose significant health risks due to factors like imaging angles and uneven contrast agent distribution. To tackle these challenges, we propose a preprocessing method that integrates Hessian-based vascular enhancement and image fusion as prerequisites for deep learning. This approach enhances fuzzy features in coronary angiography images, thereby increasing the neural network’s sensitivity to stenosis characteristics. We assessed the effectiveness of this method using the latest deep learning networks, such as YOLOv10, YOLOv9, and RT-DETR, across various evaluation metrics. Our results show that our method improves AP50 accuracy by 4.84% and 5.07% on RT-DETR R101 and YOLOv10-X, respectively, compared to images without special pre-processing. Furthermore, our analysis of different imaging angles on stenosis localization detection indicates that the left coronary artery zero is the most suitable for detecting stenosis with a AP50(%) value of 90.5. The experimental results have revealed that the proposed method is effective as a preprocessing technique for deep-learning-based coronary angiography image processing and enhances the model’s ability to identify stenosis in small blood vessels.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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