Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105280
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Health Technology and Informatics | - |
dc.creator | Ng, CKC | - |
dc.creator | Leung, VWS | - |
dc.creator | Hung, RHM | - |
dc.date.accessioned | 2024-04-12T06:51:15Z | - |
dc.date.available | 2024-04-12T06:51:15Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105280 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Ng CKC, Leung VWS, Hung RHM. Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy. Applied Sciences. 2022; 12(22):11681 is available at https://doi.org/10.3390/app122211681. | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Automation | en_US |
dc.subject | Computed tomography | en_US |
dc.subject | Image segmentation | en_US |
dc.subject | Intensity-modulated radiation therapy | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Nasopharyngeal cancer | en_US |
dc.subject | Organs at risk | en_US |
dc.subject | Radiotherapy | en_US |
dc.subject | Volumetric arc therapy | en_US |
dc.title | Clinical evaluation of deep learning and atlas-based auto-contouring for head and neck radiation therapy | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 22 | - |
dc.identifier.doi | 10.3390/app122211681 | - |
dcterms.abstract | Featured Application: Deep learning (DL) auto-contouring instead of atlas-based auto-contouring and manual contouring should be used for anatomy segmentation in head and neck radiation therapy for reducing contouring time, and commercial DL auto-contouring tools should be further trained by local hospital datasets for enhancing their geometric accuracy. 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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied sciences, Nov. 2022, v. 12, no. 22, 11681 | - |
dcterms.isPartOf | Applied sciences | - |
dcterms.issued | 2022-11 | - |
dc.identifier.scopus | 2-s2.0-85142807411 | - |
dc.identifier.eissn | 2076-3417 | - |
dc.identifier.artn | 11681 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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applsci-12-11681.pdf | 6.42 MB | Adobe PDF | View/Open |
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