Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103299
Title: Automatic detection approach for bioresorbable vascular scaffolds using a u-shaped convolutional neural network
Authors: Zhou, Wen
Chen, Fei
Zong, Yongshuo
Zhao, Dadong
Jie, Biao
Wang, Zhengdong
Huang, Chenxi
Ng, Eddie Yin Kwee
Keywords: Vascular Disease
Engineering::Mechanical engineering
Stent Implantation
Issue Date: 2019
Source: 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
Series/Report no.: IEEE Access
Abstract: 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.
URI: https://hdl.handle.net/10356/103299
http://hdl.handle.net/10220/49966
DOI: 10.1109/ACCESS.2019.2926523
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

Files in This Item:
File Description SizeFormat 
Automatic Detection Approach.pdf6.34 MBAdobe PDFThumbnail
View/Open

Page view(s)

31
checked on Sep 28, 2020

Download(s)

11
checked on Sep 28, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.