Coronary Calcium Detection Based on Improved Deep Residual Network in Mimics

Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection algorithm based on the Single Shot MultiBox Detector (SSD) in Mimics is proposed in this paper. The algori...

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Vydáno v:Journal of medical systems Ročník 43; číslo 5; s. 119 - 7
Hlavní autoři: Datong, Chen, Minghui, Liang, Cheng, Jin, Yue, Sun, Dongbin, Xu, Yueming, Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.05.2019
Springer Nature B.V
Témata:
Algorithms
Calcium
Distributed Analytics and Deep Learning in Health Care
Health Informatics
Health Sciences
Image & Signal Processing
Image detection
Image processing
Mapping
Medicine
Medicine & Public Health
Statistics for Life Sciences
Tomography
Loss function
Aggregate channel feature
Coronary calcium detection
Identity mapping
Single Shot MultiBox Detector
Resnet network
Suspected calcium area
ISSN:0148-5598, 1573-689X, 1573-689X
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Shrnutí:Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection algorithm based on the Single Shot MultiBox Detector (SSD) in Mimics is proposed in this paper. The algorithm firstly uses the aggregate channel feature model to preprocess the image to obtain the suspected calcium area, which greatly reduces the time of single-frame image detection. The basic network VGG-16 is replaced by Resnet-50, which introduces the identity mapping to solve the problem of reducing the detection accuracy when the number of network layers are increased. Finally, the powerful and flexible two-parameter loss function is used to optimize the training deep network and improve the network model generalization ability. Qualitative and quantitative experiments show that the performance of the proposed detection algorithm exceeds the existing calcium detection algorithms, and the detection efficiency is improved while ensuring the accuracy of calcium detection.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1218-4