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HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling
Medical Image Analysis ( IF 10.7 ) Pub Date : 2024-10-11 , DOI: 10.1016/j.media.2024.103374 Chen Zhao, Michele Esposito, Zhihui Xu, Weihua Zhou
Medical Image Analysis ( IF 10.7 ) Pub Date : 2024-10-11 , DOI: 10.1016/j.media.2024.103374 Chen Zhao, Michele Esposito, Zhihui Xu, Weihua Zhou
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Accurate extraction of individual arterial branches from invasive coronary angiograms (ICA) is critical for CAD diagnosis and detection of stenosis. Generating semantic segmentation for coronary arteries through deep learning-based models presents challenges due to the morphological similarity among different types of coronary arteries, making it difficult to maintain high accuracy while keeping low computational complexity. To address this challenge, we propose an innovative approach using the hyper association graph-matching neural network with uncertainty quantification (HAGMN-UQ) for coronary artery semantic labeling on ICAs. The graph-matching procedure maps the arterial branches between two individual graphs, so that the unlabeled arterial segments are classified by the labeled segments, and the coronary artery semantic labeling is achieved. Leveraging hypergraphs not only extends representation capabilities beyond pairwise relationships, but also improves the robustness and accuracy of the graph matching by enabling the modeling of higher-order associations. In addition, employing the uncertainty quantification to determine the trustworthiness of graph matching reduces the required number of comparisons, so as to accelerate the inference speed. Consequently, our model achieved an accuracy of 0.9211 for coronary artery semantic labeling with a fast inference speed, leading to an effective and efficient prediction in real-time clinical decision-making scenarios.
中文翻译:
HAGMN-UQ:冠状动脉语义标记的不确定性量化的超关联图匹配网络
冠状动脉疾病 (CAD) 是全球主要死亡原因之一。从浸润性冠状动脉造影 (ICA) 中准确提取单个动脉分支对于 CAD 诊断和狭窄检测至关重要。由于不同类型冠状动脉之间的形态相似性,通过基于深度学习的模型为冠状动脉生成语义分割带来了挑战,这使得很难在保持低计算复杂性的同时保持高精度。为了应对这一挑战,我们提出了一种创新方法,使用具有不确定性量化的超关联图匹配神经网络 (HAGMN-UQ) 对 ICA 进行冠状动脉语义标记。图形匹配程序映射两个单独图形之间的动脉分支,以便按标记的段对未标记的动脉段进行分类,并实现冠状动脉语义标记。利用超图不仅将表示功能扩展到成对关系之外,而且还通过实现高阶关联的建模来提高图形匹配的稳健性和准确性。此外,采用不确定性量化来确定图匹配的可信度减少了所需的比较次数,从而加快了推理速度。因此,我们的模型以快速的推理速度实现了 0.9211 的冠状动脉语义标记准确率,从而在实时临床决策场景中实现了有效和高效的预测。
更新日期:2024-10-11
中文翻译:
HAGMN-UQ:冠状动脉语义标记的不确定性量化的超关联图匹配网络
冠状动脉疾病 (CAD) 是全球主要死亡原因之一。从浸润性冠状动脉造影 (ICA) 中准确提取单个动脉分支对于 CAD 诊断和狭窄检测至关重要。由于不同类型冠状动脉之间的形态相似性,通过基于深度学习的模型为冠状动脉生成语义分割带来了挑战,这使得很难在保持低计算复杂性的同时保持高精度。为了应对这一挑战,我们提出了一种创新方法,使用具有不确定性量化的超关联图匹配神经网络 (HAGMN-UQ) 对 ICA 进行冠状动脉语义标记。图形匹配程序映射两个单独图形之间的动脉分支,以便按标记的段对未标记的动脉段进行分类,并实现冠状动脉语义标记。利用超图不仅将表示功能扩展到成对关系之外,而且还通过实现高阶关联的建模来提高图形匹配的稳健性和准确性。此外,采用不确定性量化来确定图匹配的可信度减少了所需的比较次数,从而加快了推理速度。因此,我们的模型以快速的推理速度实现了 0.9211 的冠状动脉语义标记准确率,从而在实时临床决策场景中实现了有效和高效的预测。