Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy
dc.contributor.author | Ng, Curtise | |
dc.contributor.author | Leung, Vincent WS | |
dc.contributor.author | Hung, Rico HM | |
dc.contributor.editor | Huang, Yujie | |
dc.date.accessioned | 2022-11-18T00:46:15Z | |
dc.date.available | 2022-11-18T00:46:15Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Ng, K.C. and Leung, V.W.S. and Hung, R.H.M. 2022. Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy. Applied Sciences. 12 (22): 11681. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/89669 | |
dc.identifier.doi | 10.3390/app122211681 | |
dc.description.abstract |
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. | |
dc.publisher | MDPI AG | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Artificial Intelligence | |
dc.subject | Automation | |
dc.subject | Computed Tomography | |
dc.subject | Image Segmentation | |
dc.subject | Intensity-Modulated Radiation Therapy | |
dc.subject | Machine Learning | |
dc.subject | Nasopharyngeal Cancer | |
dc.subject | Organs at Risk | |
dc.subject | Radiotherapy | |
dc.subject | Volumetric Arc Therapy | |
dc.title | Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy | |
dc.type | Journal Article | |
dcterms.source.volume | 12 | |
dcterms.source.number | 22 | |
dcterms.source.issn | 2076-3417 | |
dcterms.source.title | Applied Sciences | |
dc.date.updated | 2022-11-18T00:46:14Z | |
curtin.department | Curtin Medical School | |
curtin.accessStatus | Open access | |
curtin.faculty | Faculty of Health Sciences | |
curtin.contributor.orcid | Ng, Curtise [0000-0002-5849-5857] | |
curtin.contributor.researcherid | Ng, Curtise [B-2422-2013] | |
curtin.identifier.article-number | 11681 | |
curtin.contributor.scopusauthorid | Ng, Curtise [26030030100] |