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Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model

In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of ou...

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Autores principales: Hsu, Wei-Yen, Lu, Chih-Cheng, Hsu, Yuan-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676525/
https://www.ncbi.nlm.nih.gov/pubmed/33217809
http://dx.doi.org/10.1097/MD.0000000000023083
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author Hsu, Wei-Yen
Lu, Chih-Cheng
Hsu, Yuan-Yu
author_facet Hsu, Wei-Yen
Lu, Chih-Cheng
Hsu, Yuan-Yu
author_sort Hsu, Wei-Yen
collection PubMed
description In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of our study was to develop an adaptive ACM method to measure the radiological size of kidney cancer on computed tomography in the hospital patients. This paper proposed a set of medical image processing, applying images provided by the hospital and select the more obvious cases by the doctors, after the first treatment to remove noise image, and the kidney cancer contour would be circled by using the proposed adaptive ACM method. The results showed that the experimental outcome has highly similarity with the medical professional manual contour. The accuracy rate is higher than 99%. We have developed a novel adaptive ACM approach that well combines a knowledge-based system to contour the kidney cancer size in computed tomography imaging to support the clinical decision.
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spelling pubmed-76765252020-11-24 Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model Hsu, Wei-Yen Lu, Chih-Cheng Hsu, Yuan-Yu Medicine (Baltimore) 6800 In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of our study was to develop an adaptive ACM method to measure the radiological size of kidney cancer on computed tomography in the hospital patients. This paper proposed a set of medical image processing, applying images provided by the hospital and select the more obvious cases by the doctors, after the first treatment to remove noise image, and the kidney cancer contour would be circled by using the proposed adaptive ACM method. The results showed that the experimental outcome has highly similarity with the medical professional manual contour. The accuracy rate is higher than 99%. We have developed a novel adaptive ACM approach that well combines a knowledge-based system to contour the kidney cancer size in computed tomography imaging to support the clinical decision. Lippincott Williams & Wilkins 2020-11-20 /pmc/articles/PMC7676525/ /pubmed/33217809 http://dx.doi.org/10.1097/MD.0000000000023083 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 6800
Hsu, Wei-Yen
Lu, Chih-Cheng
Hsu, Yuan-Yu
Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model
title Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model
title_full Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model
title_fullStr Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model
title_full_unstemmed Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model
title_short Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model
title_sort improving segmentation accuracy of ct kidney cancer images using adaptive active contour model
topic 6800
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676525/
https://www.ncbi.nlm.nih.gov/pubmed/33217809
http://dx.doi.org/10.1097/MD.0000000000023083
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