Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy
Various commercial auto-contouring solutions have emerged over past few years to address labor-intensiveness, and inter- and intra-operator variabilities issues of traditional manual anatomy contouring for head and neck (H&N) radiation therapy (RT). The purpose of this study is to compare the cl...
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| Format: | Journal Article |
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MDPI AG
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/89669 |
| _version_ | 1848765267620921344 |
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| author | Ng, Curtise Leung, Vincent WS Hung, Rico HM |
| author2 | Huang, Yujie |
| author_facet | Huang, Yujie Ng, Curtise Leung, Vincent WS Hung, Rico HM |
| author_sort | Ng, Curtise |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Various commercial auto-contouring solutions have emerged over past few years to address labor-intensiveness, and inter- and intra-operator variabilities issues of traditional manual anatomy contouring for head and neck (H&N) radiation therapy (RT). The purpose of this study is to compare the clinical performances between RaySearch Laboratories deep learning (DL) and atlas-based auto-contouring tools for organs at risk (OARs) segmentation in the H&N RT with the manual contouring as reference. Forty-five H&N computed tomography datasets were used for the DL and atlas-based auto-contouring tools to contour 16 OARs and time required for the segmentation was measured. Dice similarity coefficient (DSC), Hausdorff distance (HD) and HD 95th-percentile (HD95) were used to evaluate geometric accuracy of OARs contoured by the DL and atlas-based auto-contouring tools. Paired sample t-test was employed to compare the mean DSC, HD, HD95, and contouring time values of the two groups. The DL auto-contouring approach achieved more consistent performance in OARs segmentation than its atlas-based approach, resulting in statistically significant time reduction of the whole segmentation process by 40% (p < 0.001). The DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values (p < 0.001–0.009) for 10 out of 16 OARs. This study proves that the RaySearch Laboratories DL auto-contouring tool has significantly better clinical performances than its atlas-based approach. |
| first_indexed | 2025-11-14T11:32:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-89669 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:32:32Z |
| publishDate | 2022 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-896692023-01-23T05:43:03Z Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy Ng, Curtise Leung, Vincent WS Hung, Rico HM Huang, Yujie Artificial Intelligence Automation Computed Tomography Image Segmentation Intensity-Modulated Radiation Therapy Machine Learning Nasopharyngeal Cancer Organs at Risk Radiotherapy Volumetric Arc Therapy Various commercial auto-contouring solutions have emerged over past few years to address labor-intensiveness, and inter- and intra-operator variabilities issues of traditional manual anatomy contouring for head and neck (H&N) radiation therapy (RT). The purpose of this study is to compare the clinical performances between RaySearch Laboratories deep learning (DL) and atlas-based auto-contouring tools for organs at risk (OARs) segmentation in the H&N RT with the manual contouring as reference. Forty-five H&N computed tomography datasets were used for the DL and atlas-based auto-contouring tools to contour 16 OARs and time required for the segmentation was measured. Dice similarity coefficient (DSC), Hausdorff distance (HD) and HD 95th-percentile (HD95) were used to evaluate geometric accuracy of OARs contoured by the DL and atlas-based auto-contouring tools. Paired sample t-test was employed to compare the mean DSC, HD, HD95, and contouring time values of the two groups. The DL auto-contouring approach achieved more consistent performance in OARs segmentation than its atlas-based approach, resulting in statistically significant time reduction of the whole segmentation process by 40% (p < 0.001). The DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values (p < 0.001–0.009) for 10 out of 16 OARs. This study proves that the RaySearch Laboratories DL auto-contouring tool has significantly better clinical performances than its atlas-based approach. 2022 Journal Article http://hdl.handle.net/20.500.11937/89669 10.3390/app122211681 http://creativecommons.org/licenses/by/4.0/ MDPI AG fulltext |
| spellingShingle | Artificial Intelligence Automation Computed Tomography Image Segmentation Intensity-Modulated Radiation Therapy Machine Learning Nasopharyngeal Cancer Organs at Risk Radiotherapy Volumetric Arc Therapy Ng, Curtise Leung, Vincent WS Hung, Rico HM Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy |
| title | Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy |
| title_full | Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy |
| title_fullStr | Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy |
| title_full_unstemmed | Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy |
| title_short | Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy |
| title_sort | clinical evaluation of deep learning and atlas-based auto-contouring for head and neck radiation therapy |
| topic | Artificial Intelligence Automation Computed Tomography Image Segmentation Intensity-Modulated Radiation Therapy Machine Learning Nasopharyngeal Cancer Organs at Risk Radiotherapy Volumetric Arc Therapy |
| url | http://hdl.handle.net/20.500.11937/89669 |