Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique

Hung Hsun Chen, Chih Min Liu, Shih Lin Chang, Paul Yu Chun Chang, Wei Shiang Chen, Yo Ming Pan, Ssu Ting Fang, Shan Quan Zhan, Chieh Mao Chuang, Yenn Jiang Lin, Ling Kuo, Mei Han Wu, Chun Ku Chen, Ying Yueh Chang, Yang Che Shiu, Shih Ann Chen*, Henry Horng Shing Lu

*Corresponding author for this work

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Background: Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create three-dimensional (3D) geometries. Methods: Five hundred eighteen patients who underwent procedures for circumferential isolation of four pulmonary veins were enrolled. Cardiac CT images (from 97 patients) were used to construct the LA detection and segmentation models. These images were reviewed by the cardiologists such that images containing the LA were identified/segmented as the ground truth for model training. Two DCNNs which incorporated transfer learning with the architectures of ResNet50/U-Net were trained for image-based LA classification/segmentation. The LA geometry created by the deep learning model was correlated to the outcomes of AF ablation. Results: The LA detection model achieved an overall 99.0% prediction accuracy, as well as a sensitivity of 99.3% and a specificity of 98.7%. Moreover, the LA segmentation model achieved an intersection over union of 91.42%. The estimated mean LA volume of all the 518 patients studied herein with the deep learning model was 123.3 ± 40.4 ml. The greatest area under the curve with a LA volume of 139 ml yielded a positive predictive value of 85.5% without detectable AF episodes over a period of one year following ablation. Conclusions: The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry.

原文English
期刊International Journal of Cardiology
DOIs
出版狀態Accepted/In press - 1 一月 2020

指紋 深入研究「Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique」主題。共同形成了獨特的指紋。

引用此