Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network

Artificial stent implantation is one of the most effective ways to treat vascular diseases. However, commonly used metal stents have many negative effects, such as being difficult to remove and recover, whereas bio-absorbable stents have become the best way to treat vascular diseases because of thei...

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Main Authors: Zhou, Wen, Chen, Fei, Zong, Yongshuo, Zhao, Dadong, Jie, Biao, Wang, Zhengdong, Huang, Chenxi, Ng, Eddie Yin Kwee
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/103299
http://hdl.handle.net/10220/49966
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1032992023-03-04T17:20:04Z Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network Zhou, Wen Chen, Fei Zong, Yongshuo Zhao, Dadong Jie, Biao Wang, Zhengdong Huang, Chenxi Ng, Eddie Yin Kwee School of Mechanical and Aerospace Engineering Vascular Disease Engineering::Mechanical engineering Stent Implantation Artificial stent implantation is one of the most effective ways to treat vascular diseases. However, commonly used metal stents have many negative effects, such as being difficult to remove and recover, whereas bio-absorbable stents have become the best way to treat vascular diseases because of their absorbability and harmlessness. It is very important in vascular medical imaging, such as optical coherence tomography (OCT), to be able to effectively track the position of stents in blood vessels. This task is undoubtedly labor-intensive, and it is inefficient to rely on experts to identify various scaffolds from medical images. In this paper, a novel automatic detection method for bioresorbable vascular scaffolds (BVSs) via a U-shaped convolutional neural network is developed. The method is composed of three steps: data preparation, network training, and network testing. First, in the data preparation step, we complete the task of labeling related samples based on expert experience, and then, these labeled OCT images are divided into the original and masked OCT images (corresponding to X and Y in supervised learning, respectively). Next, we train our data on a U-shaped convolutional neural network, which consists of five downsampling modules and four upsampling modules. We can obtain a related training model, which can be used to predict the related samples. In the testing stage, we can easily utilize the trained model to predict the input OCT data so that we can obtain the relevant information about a BVS in an OCT image. Obviously, this method can assist doctors in diagnosing the disease and in making important decisions. Finally, some experiments are performed to validate our proposed method, and the IoU criterion is used to measure the superiority of our proposed method. The results show that our proposed method is completely feasible and superior. Published version 2019-09-19T04:47:54Z 2019-12-06T21:09:25Z 2019-09-19T04:47:54Z 2019-12-06T21:09:25Z 2019 Journal Article Zhou, W., Chen, F., Zong, Y., Zhao, D., Jie, B., Wang, Z., . . . Ng, E. Y. K. (2019). Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network. IEEE Access, 7, 94424-94430. doi:10.1109/ACCESS.2019.2926523 https://hdl.handle.net/10356/103299 http://hdl.handle.net/10220/49966 10.1109/ACCESS.2019.2926523 en IEEE Access © 2019 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license*, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given. 7 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Vascular Disease
Engineering::Mechanical engineering
Stent Implantation
spellingShingle Vascular Disease
Engineering::Mechanical engineering
Stent Implantation
Zhou, Wen
Chen, Fei
Zong, Yongshuo
Zhao, Dadong
Jie, Biao
Wang, Zhengdong
Huang, Chenxi
Ng, Eddie Yin Kwee
Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
description Artificial stent implantation is one of the most effective ways to treat vascular diseases. However, commonly used metal stents have many negative effects, such as being difficult to remove and recover, whereas bio-absorbable stents have become the best way to treat vascular diseases because of their absorbability and harmlessness. It is very important in vascular medical imaging, such as optical coherence tomography (OCT), to be able to effectively track the position of stents in blood vessels. This task is undoubtedly labor-intensive, and it is inefficient to rely on experts to identify various scaffolds from medical images. In this paper, a novel automatic detection method for bioresorbable vascular scaffolds (BVSs) via a U-shaped convolutional neural network is developed. The method is composed of three steps: data preparation, network training, and network testing. First, in the data preparation step, we complete the task of labeling related samples based on expert experience, and then, these labeled OCT images are divided into the original and masked OCT images (corresponding to X and Y in supervised learning, respectively). Next, we train our data on a U-shaped convolutional neural network, which consists of five downsampling modules and four upsampling modules. We can obtain a related training model, which can be used to predict the related samples. In the testing stage, we can easily utilize the trained model to predict the input OCT data so that we can obtain the relevant information about a BVS in an OCT image. Obviously, this method can assist doctors in diagnosing the disease and in making important decisions. Finally, some experiments are performed to validate our proposed method, and the IoU criterion is used to measure the superiority of our proposed method. The results show that our proposed method is completely feasible and superior.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Zhou, Wen
Chen, Fei
Zong, Yongshuo
Zhao, Dadong
Jie, Biao
Wang, Zhengdong
Huang, Chenxi
Ng, Eddie Yin Kwee
format Article
author Zhou, Wen
Chen, Fei
Zong, Yongshuo
Zhao, Dadong
Jie, Biao
Wang, Zhengdong
Huang, Chenxi
Ng, Eddie Yin Kwee
author_sort Zhou, Wen
title Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
title_short Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
title_full Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
title_fullStr Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
title_full_unstemmed Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
title_sort automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
publishDate 2019
url https://hdl.handle.net/10356/103299
http://hdl.handle.net/10220/49966
_version_ 1759853064056995840