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

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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Main Authors: 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
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Head and neck cancer
Radiotherapy
Deep-learning auto-segmentation models
Orodental structures
Osteoradionecrosis
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631625001228
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