Abstract：
OBJECTIVE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA).
METHODS: A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers.
RESULTS: A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers\' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%.
CONCLUSIONS: The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.
METHODS: A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers.
RESULTS: A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers\' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%.
CONCLUSIONS: The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.
摘要：
目的：根据心血管计算机断层扫描（CT）学会关于冠状动脉CT血管造影（CCTA）的指南，评估一种新的基于深度学习（DL）的自动冠状动脉标记方法，用于冠状动脉疾病的结构化报告。
方法：104例患者的回顾性队列（60.3±10.7年，61％的男性）接受了前瞻性心电图同步CCTA。自动提取冠状动脉中心线,贴上标签，并由2位专家读者根据心血管CT指南进行验证。DL算法在706个放射科医师注释的病例上进行了训练，以自动标记冠状动脉中心线。该架构利用树结构的长短期记忆递归神经网络，通过使用两步方法捕获冠状动脉树的全部拓扑信息：自底向上编码步骤，然后是自上而下的解码步骤。第一模块将每个子树编码为固定大小的向量表示。解码模块然后选择性地关注聚合的全局上下文以执行标签的局部分配。为了评估软件的性能，计算算法标签和专家读者之间的重叠百分比。
结果：共鉴定出1491个片段。与专家读者的标签相比，基于人工智能的软件方法的平均重叠率为94.4％，范围从右冠状动脉后降支的87.1％到右冠状动脉近段的100％。平均计算时间为0.5秒/例。中间阅读器重叠为96.6%。
结论：提出的基于DL的全自动冠状动脉标记算法提供了快速和精确的冠状动脉段标记，具有改善CCTA自动化结构化报告的潜力。
方法：104例患者的回顾性队列（60.3±10.7年，61％的男性）接受了前瞻性心电图同步CCTA。自动提取冠状动脉中心线,贴上标签，并由2位专家读者根据心血管CT指南进行验证。DL算法在706个放射科医师注释的病例上进行了训练，以自动标记冠状动脉中心线。该架构利用树结构的长短期记忆递归神经网络，通过使用两步方法捕获冠状动脉树的全部拓扑信息：自底向上编码步骤，然后是自上而下的解码步骤。第一模块将每个子树编码为固定大小的向量表示。解码模块然后选择性地关注聚合的全局上下文以执行标签的局部分配。为了评估软件的性能，计算算法标签和专家读者之间的重叠百分比。
结果：共鉴定出1491个片段。与专家读者的标签相比，基于人工智能的软件方法的平均重叠率为94.4％，范围从右冠状动脉后降支的87.1％到右冠状动脉近段的100％。平均计算时间为0.5秒/例。中间阅读器重叠为96.6%。
结论：提出的基于DL的全自动冠状动脉标记算法提供了快速和精确的冠状动脉段标记，具有改善CCTA自动化结构化报告的潜力。
