Young Researcher Paper Award 2023
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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.
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.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 34, Number 4(1) (2022)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1287-1296
S&M2891 Research Paper of Special Issue https://doi.org/10.18494/SAM3503 Published: April 4, 2022 Ejection Fraction Measurement and Regional Wall Motion Abnormality Assessment Using Deep-learning Neural Networks in Left Ventriculography [PDF] 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 (LVG) 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 (RWMA) assessment through LVG. Our methods can automatically segment the end-diastolic and end-systolic areas for clinicians and perform EF measurement and RWMA assessment in real time. Semantic segmentation neural networks were implemented for EF measurement, and image convolution neural networks were implemented in RWMA recognition. LVG 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 RWMA assessment were performed with high accuracy.
Corresponding author: Yung-Yao Chen  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Shan-Bin Chan, Yuan-Chun Lai, Wei-Ting Chang, Kuo-Ting Tang, Ming-Shih Huang, Zhih-Cheng Chen, and Yung-Yao Chen, Ejection Fraction Measurement and Regional Wall Motion Abnormality Assessment Using Deep-learning Neural Networks in Left Ventriculography, Sens. Mater., Vol. 34, No. 4, 2022, p. 1287-1296. |
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