Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment
Background and purpose: Accurate delineation of orodental structures on radiotherapy computed tomography (CT) images is essential for dosimetric assessment and dental decisions. We propose a deep-learning (DL) auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned...
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2025-07-01
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| author | Laia Humbert-Vidan Austin H. Castelo Renjie He Lisanne V. van Dijk Dong Joo Rhee Congjun Wang He C. Wang Kareem A. Wahid Sonali Joshi Parshan Gerafian Natalie West Zaphanlene Kaffey Sarah Mirbahaeddin Jaqueline Curiel Samrina Acharya Amal Shekha Praise Oderinde Alaa M.S. Ali Andrew Hope Erin Watson Ruth Wesson-Aponte Steven J. Frank Carly E.A. Barbon Kristy K. Brock Mark S. Chambers Muhammad Walji Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno Renjie He Steven J. Frank Carly E.A. Barbon Kristy K. Brock Mark S. Chambers Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno Laia Humbert-Vidan Renjie He Kareem A. Wahid Natalie West Zaphanlene Kaffey Alaa M.S. Ali Ruth Wesson-Aponte Carly E.A. Barbon Mark S. Chambers Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno |
| author_facet | Laia Humbert-Vidan Austin H. Castelo Renjie He Lisanne V. van Dijk Dong Joo Rhee Congjun Wang He C. Wang Kareem A. Wahid Sonali Joshi Parshan Gerafian Natalie West Zaphanlene Kaffey Sarah Mirbahaeddin Jaqueline Curiel Samrina Acharya Amal Shekha Praise Oderinde Alaa M.S. Ali Andrew Hope Erin Watson Ruth Wesson-Aponte Steven J. Frank Carly E.A. Barbon Kristy K. Brock Mark S. Chambers Muhammad Walji Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno Renjie He Steven J. Frank Carly E.A. Barbon Kristy K. Brock Mark S. Chambers Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno Laia Humbert-Vidan Renjie He Kareem A. Wahid Natalie West Zaphanlene Kaffey Alaa M.S. Ali Ruth Wesson-Aponte Carly E.A. Barbon Mark S. Chambers Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno |
| author_sort | Laia Humbert-Vidan |
| collection | DOAJ |
| description | Background and purpose: Accurate delineation of orodental structures on radiotherapy computed tomography (CT) images is essential for dosimetric assessment and dental decisions. We propose a deep-learning (DL) auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned with the ClinRad osteoradionecrosis staging system. Materials and methods: Mandible and maxilla sub-volumes were manually defined on simulation CT images from 60 clinical cases, differentiating alveolar from basal regions; teeth were labelled individually. For each task, a DL segmentation model was independently trained. A Swin UNETR-based model was used for mandible sub-volumes. For smaller structures (e.g., teeth and maxilla sub-volumes) a two-stage model first used the ResUNet to segment the entire teeth and maxilla regions as a single ROI used to crop the image input for Swin UNETR. In addition to segmentation accuracy and geometric precision, a dose-volume comparison was made between manual and model-predicted segmentations. Results: Segmentation performance varied across sub-volumes – mean Dice values of 0.85 (mandible basal), 0.82 (mandible alveolar), 0.78 (maxilla alveolar), 0.80 (upper central teeth), 0.69 (upper premolars), 0.76 (upper molars), 0.76 (lower central teeth), 0.70 (lower premolars), 0.71 (lower molars) – with limited applicability in segmenting sub-volumes absent in the data. The maxilla alveolar central sub-volume showed a statistically significant dose-volume difference in both Dmean and D2%. Conclusions: We present a novel DL-based auto-segmentation framework of orodental structures, enabling spatial localization of dose-related differences. This tool may enhance image-based bone injury detection and improve clinical decision-making in radiation oncology and dental care for head and neck cancer patients. |
| format | Article |
| id | doaj-art-1bbbbd654ba44d6da871b0e5f3724db4 |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-1bbbbd654ba44d6da871b0e5f3724db42025-08-23T04:48:44ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162025-07-013510081710.1016/j.phro.2025.100817Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessmentLaia Humbert-Vidan0Austin H. Castelo1Renjie He2Lisanne V. van Dijk3Dong Joo Rhee4Congjun Wang5He C. Wang6Kareem A. Wahid7Sonali Joshi8Parshan Gerafian9Natalie West10Zaphanlene Kaffey11Sarah Mirbahaeddin12Jaqueline Curiel13Samrina Acharya14Amal Shekha15Praise Oderinde16Alaa M.S. Ali17Andrew Hope18Erin Watson19Ruth Wesson-Aponte20Steven J. Frank21Carly E.A. Barbon22Kristy K. Brock23Mark S. Chambers24Muhammad Walji25Katherine A. Hutcheson26Stephen Y. Lai27Clifton D. Fuller28Mohamed A. Naser29Amy C. Moreno30Renjie He31Steven J. Frank32Carly E.A. Barbon33Kristy K. Brock34Mark S. Chambers35Katherine A. Hutcheson36Stephen Y. Lai37Clifton D. Fuller38Mohamed A. Naser39Amy C. Moreno40Laia Humbert-Vidan41Renjie He42Kareem A. Wahid43Natalie West44Zaphanlene Kaffey45Alaa M.S. Ali46Ruth Wesson-Aponte47Carly E.A. Barbon48Mark S. Chambers49Katherine A. Hutcheson50Stephen Y. Lai51Clifton D. Fuller52Mohamed A. Naser53Amy C. Moreno54Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Oncology, University Medical Center Groningen, Groningen, NetherlandsDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Radiation Oncology, Princess Margaret Cancer Center, Toronto, CA, USADepartment of Dental Oncology, Princess Margaret Cancer Center, Toronto, CA, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Clinical and Health Informatics, Texas Center of Oral Health Care Quality & Safety, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Corresponding author at: H&N Service, Department of Radiation Oncology, UT MD Anderson Cancer Center, USA.Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USADivision of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USABackground and purpose: Accurate delineation of orodental structures on radiotherapy computed tomography (CT) images is essential for dosimetric assessment and dental decisions. We propose a deep-learning (DL) auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned with the ClinRad osteoradionecrosis staging system. Materials and methods: Mandible and maxilla sub-volumes were manually defined on simulation CT images from 60 clinical cases, differentiating alveolar from basal regions; teeth were labelled individually. For each task, a DL segmentation model was independently trained. A Swin UNETR-based model was used for mandible sub-volumes. For smaller structures (e.g., teeth and maxilla sub-volumes) a two-stage model first used the ResUNet to segment the entire teeth and maxilla regions as a single ROI used to crop the image input for Swin UNETR. In addition to segmentation accuracy and geometric precision, a dose-volume comparison was made between manual and model-predicted segmentations. Results: Segmentation performance varied across sub-volumes – mean Dice values of 0.85 (mandible basal), 0.82 (mandible alveolar), 0.78 (maxilla alveolar), 0.80 (upper central teeth), 0.69 (upper premolars), 0.76 (upper molars), 0.76 (lower central teeth), 0.70 (lower premolars), 0.71 (lower molars) – with limited applicability in segmenting sub-volumes absent in the data. The maxilla alveolar central sub-volume showed a statistically significant dose-volume difference in both Dmean and D2%. Conclusions: We present a novel DL-based auto-segmentation framework of orodental structures, enabling spatial localization of dose-related differences. This tool may enhance image-based bone injury detection and improve clinical decision-making in radiation oncology and dental care for head and neck cancer patients.http://www.sciencedirect.com/science/article/pii/S2405631625001228Head and neck cancerRadiotherapyDeep-learning auto-segmentation modelsOrodental structuresOsteoradionecrosis |
| spellingShingle | Laia Humbert-Vidan Austin H. Castelo Renjie He Lisanne V. van Dijk Dong Joo Rhee Congjun Wang He C. Wang Kareem A. Wahid Sonali Joshi Parshan Gerafian Natalie West Zaphanlene Kaffey Sarah Mirbahaeddin Jaqueline Curiel Samrina Acharya Amal Shekha Praise Oderinde Alaa M.S. Ali Andrew Hope Erin Watson Ruth Wesson-Aponte Steven J. Frank Carly E.A. Barbon Kristy K. Brock Mark S. Chambers Muhammad Walji Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno Renjie He Steven J. Frank Carly E.A. Barbon Kristy K. Brock Mark S. Chambers Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno Laia Humbert-Vidan Renjie He Kareem A. Wahid Natalie West Zaphanlene Kaffey Alaa M.S. Ali Ruth Wesson-Aponte Carly E.A. Barbon Mark S. Chambers Katherine A. Hutcheson Stephen Y. Lai Clifton D. Fuller Mohamed A. Naser Amy C. Moreno Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment Physics and Imaging in Radiation Oncology Head and neck cancer Radiotherapy Deep-learning auto-segmentation models Orodental structures Osteoradionecrosis |
| title | Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment |
| title_full | Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment |
| title_fullStr | Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment |
| title_full_unstemmed | Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment |
| title_short | Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment |
| title_sort | image based mandibular and maxillary parcellation and annotation using computed tomography impact a deep learning based clinical tool for orodental dose estimation and osteoradionecrosis assessment |
| topic | Head and neck cancer Radiotherapy Deep-learning auto-segmentation models Orodental structures Osteoradionecrosis |
| url | http://www.sciencedirect.com/science/article/pii/S2405631625001228 |
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