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Title: | Clinical evaluation of deep learning and atlas-based auto-contouring for head and neck radiation therapy | Authors: | Ng, CKC Leung, VWS Hung, RHM |
Issue Date: | Nov-2022 | Source: | Applied sciences, Nov. 2022, v. 12, no. 22, 11681 | 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. | Keywords: | Artificial intelligence Automation Computed tomography Image segmentation Intensity-modulated radiation therapy Machine learning Nasopharyngeal cancer Organs at risk Radiotherapy Volumetric arc therapy |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Applied sciences | EISSN: | 2076-3417 | DOI: | 10.3390/app122211681 | 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/). 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. |
Appears in Collections: | Journal/Magazine Article |
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applsci-12-11681.pdf | 6.42 MB | Adobe PDF | View/Open |
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