Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography.
Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography.
复制标题
DOI：
10.3346/jkms.2023.38.e306
复制
发表时间：
2023-09-18
影响因子：
4.5
通讯作者：
Guang, Yang
中科院分区：
文献类型：
--
作者：
Yongwon, Cho;Soojung, Park;Ho, Hwang Sung;Minseok, Ko;Do-Sun, Lim;Woong, Yu Cheol;Seong-Mi, Park;Mi-Na, Kim;Yu-Whan, Oh;Guang, Yang
提出一种深度学习架构，用于经导管主动脉瓣置换术（TAVR）中使用心脏计算机断层扫描（CT）自动检测主动脉环平面的复杂结构。本研究回顾性回顾了2017年1月至2020年7月在三级医疗中心连续接受TAVR的患者。建立了基于三维U-net结构的主动脉环检测置换网络（ADPANet），用于心脏CT主动脉环平面的检测和定位。纳入了2017年1月至2020年7月在三级医疗中心接受TAVR的患者（N = 72）。使用有限数据集的地面真相由三名心脏放射科医生手动划定。训练集、调优集和测试集（70:10:20）用于构建深度学习模型。采用均方根误差（RMSE）和骰子相似系数（DSC）分析了ADPANet检测主动脉环平面的性能。在这项研究中，总数据集包括72个选择的TAVR患者的扫描。ADPANet对主动脉环平面的RMSE和DSC值分别为55.078±35.794和0.496±0.217。我们的深度学习框架可用于心脏CT TAVR检测主动脉环平面三维复杂结构。我们的算法性能优于其他卷积神经网络。
To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.
登录
查看更多内容
影响因子：
10.9
作者：
Huang, Xiaojie;Dione, Donald P.;Compas, Colin B.;Papademetris, Xenophon;Lin, Ben A.;Bregasi, Alda;Sinusas, Albert J.;Staib, Lawrence H.;Duncan, James S.
通讯作者：
Duncan, James S.
影响因子：
2.4
作者：
Morgan, Timothy G.;Bostrom, Mathias P. G.;van der Meulen, Marjolein C. H.
通讯作者：
van der Meulen, Marjolein C. H.
DOI：
10.1007/978-3-642-40811-3_61
发表时间：
2013
期刊：
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
Pouch, Alison M.;Wang, Hongzhi;Takabe, Manabu;Jackson, Benjamin M.;Sehgal, Chandra M.;Gorman, Joseph H., III;Gorman, Robert C.;Yushkevich, Paul A.
通讯作者：
Yushkevich, Paul A.
影响因子：
10.9
作者：
Schneider, Robert J.;Perrin, Douglas P.;Vasilyev, Nikolay V.;Marx, Gerald R.;del Nido, Pedro J.;Howe, Robert D.
通讯作者：
Howe, Robert D.