PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks

Rongjun Ge; Guanyu Yang; Yang Chen; Limin Luo; Cheng Feng; Heye Zhang; Shuo Li

doi:10.1016/j.media.2019.101554

PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks

Med Image Anal. 2019 Dec:58:101554. doi: 10.1016/j.media.2019.101554. Epub 2019 Sep 10.

Authors

Rongjun Ge¹, Guanyu Yang¹, Yang Chen², Limin Luo¹, Cheng Feng³, Heye Zhang⁴, Shuo Li⁵

Affiliations

¹ Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France.
² Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China; School of Cyber Science and Engineering, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France. Electronic address: chenyang.list@seu.edu.cn.
³ Department of Ultrasound, The Third People's Hospital of Shenzhen, Shenzhen, China.
⁴ School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China.
⁵ Department of Medical Imaging, Western University, London, Canada; Digital Imaging Group of London, London, Canada. Electronic address: sli287@uwo.ca.

PMID: 31546227
DOI: 10.1016/j.media.2019.101554

Abstract

Accurate direct estimation of the left ventricle (LV) multitype indices from two-dimensional (2D) echocardiograms of paired apical views, i.e., paired apical four-chamber (A4C) and two-chamber (A2C), is of great significance to clinically evaluate cardiac function. It enables a comprehensive assessment from multiple dimensions and views. Yet it is extremely challenging and has never been attempted, due to significantly varied LV shape and appearance across subjects and along cardiac cycle, the complexity brought by the paired different views, unexploited inter-frame indices relatedness hampering working effect, and low image quality preventing segmentation. We propose a paired-views LV network (PV-LVNet) to automatically and directly estimate LV multitype indices from paired echo apical views. Based on a newly designed Res-circle Net, the PV-LVNet robustly locates LV and automatically crops LV region of interest from A4C and A2C sequence with location module and image resampling, then accurately and consistently estimates 7 different indices of multiple dimensions (1D, 2D & 3D) and views (A2C, A4C, and union of A2C+A4C) with indices module. The experiments show that our method achieves high performance with accuracy up to 2.85mm mean absolute error and internal consistency up to 0.974 Cronbach's α for the cardiac indices estimation. All of these indicate that our method enables an efficient, accurate and reliable cardiac function diagnosis in clinical.

Keywords: 2D echo; Direct estimation; Multitype cardiac indices; Paired apical views; Res-circle Net.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Echocardiography*
Heart Ventricles / diagnostic imaging*
Humans
Image Enhancement / methods*
Neural Networks, Computer*