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Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy
Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists’ labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the co...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932790/ https://www.ncbi.nlm.nih.gov/pubmed/36788411 http://dx.doi.org/10.1177/15330338231157936 |
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author | Luan, Shunyao Xue, Xudong Wei, Changchao Ding, Yi Zhu, Benpeng Wei, Wei |
author_facet | Luan, Shunyao Xue, Xudong Wei, Changchao Ding, Yi Zhu, Benpeng Wei, Wei |
author_sort | Luan, Shunyao |
collection | PubMed |
description | Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists’ labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the contours generated by the CNNs. Besides, all the evaluation criteria, such as Dice Similarity Coefficient (DSC), need a gold standard to assess the quality of the contours. To address these problems, we propose an automatic quality assurance (QA) method using isotropic and anisotropic methods to automatically analyze contour quality without a gold standard. Materials/Methods: We used 196 individuals with 18 different head-and-neck organs-at-risk. The overall process has the following 4 main steps. (1) Use CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. (2) Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells’ radiomics features ratios and DSCs as the input for 12 machine learning models. (3) Using the DSC threshold adaptively classified the passing/un-passing slices. (4) Through 2 different threshold analysis methods quantitatively evaluated the un-passing slices and obtained a series of location information of poor contours. Parts 1-3 were isotropic experiments, and part 4 was the anisotropic method. Result: From the isotropic experiments, almost all the predicted values were close to the labels. Through the anisotropic method, we obtained the contours’ location information by assessing the thresholds of the peak-to-peak and area-to-area ratios. Conclusion: The proposed automatic segmentation QA method could predict the segmentation quality qualitatively. Moreover, the method can analyze the location information for un-passing slices. |
format | Online Article Text |
id | pubmed-9932790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-99327902023-02-17 Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy Luan, Shunyao Xue, Xudong Wei, Changchao Ding, Yi Zhu, Benpeng Wei, Wei Technol Cancer Res Treat Novel applications of Artificial Intelligence in cancer research Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists’ labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the contours generated by the CNNs. Besides, all the evaluation criteria, such as Dice Similarity Coefficient (DSC), need a gold standard to assess the quality of the contours. To address these problems, we propose an automatic quality assurance (QA) method using isotropic and anisotropic methods to automatically analyze contour quality without a gold standard. Materials/Methods: We used 196 individuals with 18 different head-and-neck organs-at-risk. The overall process has the following 4 main steps. (1) Use CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. (2) Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells’ radiomics features ratios and DSCs as the input for 12 machine learning models. (3) Using the DSC threshold adaptively classified the passing/un-passing slices. (4) Through 2 different threshold analysis methods quantitatively evaluated the un-passing slices and obtained a series of location information of poor contours. Parts 1-3 were isotropic experiments, and part 4 was the anisotropic method. Result: From the isotropic experiments, almost all the predicted values were close to the labels. Through the anisotropic method, we obtained the contours’ location information by assessing the thresholds of the peak-to-peak and area-to-area ratios. Conclusion: The proposed automatic segmentation QA method could predict the segmentation quality qualitatively. Moreover, the method can analyze the location information for un-passing slices. SAGE Publications 2023-02-14 /pmc/articles/PMC9932790/ /pubmed/36788411 http://dx.doi.org/10.1177/15330338231157936 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Novel applications of Artificial Intelligence in cancer research Luan, Shunyao Xue, Xudong Wei, Changchao Ding, Yi Zhu, Benpeng Wei, Wei Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy |
title | Machine Learning-Based Quality Assurance for Automatic Segmentation
of Head-and-Neck Organs-at-Risk in Radiotherapy |
title_full | Machine Learning-Based Quality Assurance for Automatic Segmentation
of Head-and-Neck Organs-at-Risk in Radiotherapy |
title_fullStr | Machine Learning-Based Quality Assurance for Automatic Segmentation
of Head-and-Neck Organs-at-Risk in Radiotherapy |
title_full_unstemmed | Machine Learning-Based Quality Assurance for Automatic Segmentation
of Head-and-Neck Organs-at-Risk in Radiotherapy |
title_short | Machine Learning-Based Quality Assurance for Automatic Segmentation
of Head-and-Neck Organs-at-Risk in Radiotherapy |
title_sort | machine learning-based quality assurance for automatic segmentation
of head-and-neck organs-at-risk in radiotherapy |
topic | Novel applications of Artificial Intelligence in cancer research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9932790/ https://www.ncbi.nlm.nih.gov/pubmed/36788411 http://dx.doi.org/10.1177/15330338231157936 |
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