Urinary Stones Segmentation Model and AI Web Application Development in Abdominal CT Images Through Machine Learning


KIPS Transactions on Computer and Communication Systems, Vol. 10, No. 11, pp. 305-310, Nov. 2021
https://doi.org/10.3745/KTCCS.2021.10.11.305,   PDF Download:
Keywords: Urinary Stone, DICOM, Artificial intelligence, Model Serving, Flask
Abstract

Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fullyconvolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.


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Cite this article
[IEEE Style]
L. Chung-Sub, L. Dong-Wook, N. Si-Hyeong, K. Tae-Hoon, P. Sung-Bin, Y. Kwon-Ha, J. Chang-Won, "Urinary Stones Segmentation Model and AI Web Application Development in Abdominal CT Images Through Machine Learning," KIPS Transactions on Computer and Communication Systems, vol. 10, no. 11, pp. 305-310, 2021. DOI: https://doi.org/10.3745/KTCCS.2021.10.11.305.

[ACM Style]
Lee Chung-Sub, Lim Dong-Wook, Noh Si-Hyeong, Kim Tae-Hoon, Park Sung-Bin, Yoon Kwon-Ha, and Jeong Chang-Won. 2021. Urinary Stones Segmentation Model and AI Web Application Development in Abdominal CT Images Through Machine Learning. KIPS Transactions on Computer and Communication Systems, 10, 11, (2021), 305-310. DOI: https://doi.org/10.3745/KTCCS.2021.10.11.305.