S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Ejection Fraction Measurement and Regional Wall Motion Abnormality Assessment Using Deep-learning Neural Networks in Left Ventriculography

Shan-Bin Chan, Yuan-Chun Lai, Wei-Ting Chang, Kuo-Ting Tang, Ming-Shih Huang, Zhih-Cheng Chen, and Yung-Yao Chen

(Received July 4, 2021; Accepted September 30, 2021)

Keywords: ejection fraction, regional wall motion abnormalities, deep learning, neural networks, left ventriculography, semantic segmentation, image classification

In this research, an x-ray flat panel detector is adopted as an image collection sensor for evaluating left ventricular systolic functions. Typically, left ventriculography is conducted in the end-diastolic and end-systolic areas by clinicians, which is time-consuming and the calculated ejection fraction (EF) varies among clinicians. We propose two novel methods for EF measurement and regional wall motion abnormality assessment through left ventriculography. Our methods can automatically segment the end-diastolic and end-systolic areas for clinicians and perform EF measurement and regional wall motion abnormality assessment in real-time. Semantic segmentation neural networks were implemented for EF measurement, and image convolution neural networks were implemented in regional wall motion abnormality recognition. Left ventriculography images were collected by clinicians, but the data set labeling procedure was not performed by clinicians. This method may reduce the need for medical doctors in the data set labeling procedure. Using the proposed methods, EF measurement and regional wall motion abnormality assessment were performed with high accuracy.

Corresponding author: Yung-Yao Chen




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