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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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11681 |
| _version_ | 1797465952802045952 |
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| author | Curtise K. C. Ng Vincent W. S. Leung Rico H. M. Hung |
| author_facet | Curtise K. C. Ng Vincent W. S. Leung Rico H. M. Hung |
| author_sort | Curtise K. C. Ng |
| collection | DOAJ |
| 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 <i>t</i>-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% (<i>p</i> < 0.001). The DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values (<i>p</i> < 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 | 2024-03-09T18:28:56Z |
| format | Article |
| id | doaj.art-02b5956c7ceb4495ba2a9c75cd239674 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:28:56Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-02b5956c7ceb4495ba2a9c75cd2396742023-11-24T07:39:15ZengMDPI AGApplied Sciences2076-34172022-11-0112221168110.3390/app122211681Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation TherapyCurtise K. C. Ng0Vincent W. S. Leung1Rico H. M. Hung2Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, AustraliaDepartment of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Clinical Oncology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, ChinaVarious 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 <i>t</i>-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% (<i>p</i> < 0.001). The DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values (<i>p</i> < 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.https://www.mdpi.com/2076-3417/12/22/11681artificial intelligenceautomationcomputed tomographyimage segmentationintensity-modulated radiation therapymachine learning |
| spellingShingle | Curtise K. C. Ng Vincent W. S. Leung Rico H. M. Hung Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy Applied Sciences artificial intelligence automation computed tomography image segmentation intensity-modulated radiation therapy machine learning |
| 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 |
| url | https://www.mdpi.com/2076-3417/12/22/11681 |
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