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

Autor: Heye Zhang, Cheng Feng, Rongjun Ge, Yang Chen, Shuo Li, Limin Luo, Guanyu Yang
Přispěvatelé: Nanjing Southeast University, Laboratory of Image Science and Technology [Nanjing] (LIST), Southeast University [Jiangsu]-School of Computer Science and Engineering, Sun Yat-Sen University [Guangzhou] (SYSU), Western University (UWO), 201706090248, China Scholarship Council, 81530060, National Natural Science Foundation of China, 2018B030333001, Science and Technology Planning Project of Guangdong Province, Nanjing Southeast University (SEU)
Rok vydání: 2018
Předmět:
Image quality
Computer science
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging
Heart Ventricles
Health Informatics
Paired apical views
Direct estimation
030218 nuclear medicine & medical imaging
2D echo
03 medical and health sciences
0302 clinical medicine
[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system
Region of interest
Image scaling
medicine
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Estimation
Radiological and Ultrasound Technology
Cardiac cycle
Res-circle Net
business.industry
Pattern recognition
Multitype cardiac indices
Image Enhancement
Computer Graphics and Computer-Aided Design
medicine.anatomical_structure
Ventricle
Echocardiography
Deep neural networks
Computer Vision and Pattern Recognition
Artificial intelligence
Neural Networks
Computer
business
030217 neurology & neurosurgery
Zdroj: Medical Image Analysis
Medical Image Analysis, Elsevier, 2019, 58, pp.101554. ⟨10.1016/j.media.2019.101554⟩
Medical Image Analysis, 2019, 58, pp.101554. ⟨10.1016/j.media.2019.101554⟩
ISSN: 1361-8423
1361-8415
Popis: International audience; 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 and 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.
Databáze: OpenAIRE
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