TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography.
TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography.
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DOI：
10.1109/jtehm.2023.3329031
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发表时间：
2024
影响因子：
3.4
通讯作者：
中科院分区：
文献类型：
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作者：
目的：现有的心脏CT血管造影冠状动脉分支自动标记方法存在两个局限性：1)无法建立分支的整体相关性模型，无法直接捕捉分支之间的差异。2)主分支和副分支之间严重的类不平衡。方法和步骤：受Transformer在序列数据中的应用启发，我们提出了一种拓扑Transformer网络（TTN），该网络从序列标记学习的新角度解决了血管分支标记问题。TTN通过建立分支之间的总体相关性来检测分支之间的差异。提出了一种表示动脉树中血管段位置的拓扑编码，以帮助模型对分支进行分类。此外，还引入了段深度损失来解决主分支和副分支之间的类不平衡问题。结果：在具有325个CCTA的数据集上，我们的方法在所有分支上获得了最佳的总体结果，在侧分支上获得了最佳结果，在主分支上获得了竞争结果。结论：TTN很好地解决了现有方法的两个局限性，从而在冠状动脉分支标记任务中取得了最佳效果。这是第一个基于变压器的管道分支标记方法，与以往的方法有明显的不同。临床影响：本临床前研究可整合到计算机辅助诊断系统中生成心血管疾病诊断报告，协助临床医生定位动脉粥样硬化斑块。
Objective: Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches. Methods and procedures: Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning. TTN detects differences between branches by establishing their overall correlation. A topological encoding that represents the positions of vessel segments in the artery tree, is proposed to assist the model in classifying branches. Also, a segment-depth loss is introduced to solve the class imbalance between main and side branches. Results: On a dataset with 325 CCTA, our method obtains the best overall result on all branches, the best result on side branches, and a competitive result on main branches. Conclusion: TTN solves two limitations in existing methods perfectly, thus achieving the best result in coronary artery branch labeling task. It is the first Transformer based vessel branch labeling method and is notably different from previous methods. Clinical impact: This Pre-Clinical Research can be integrated into a computer-aided diagnosis system to generate cardiovascular disease diagnosis report, assisting clinicians in locating the atherosclerotic plaques.
使用信仰传播对脑动脉进行自动解剖标记。
DOI：
10.1117/12.2006460
发表时间：
2013-03-13
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
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作者：
Bilgel M;Roy S;Carass A;Nyquist PA;Prince JL
通讯作者：
Prince JL