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.
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Ошибки и ограничения
История · было → стало
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Humbert-Vidan Laia, Castelo Austin H, He Renjie, van Dijk Lisanne V, Rhee Dong Joo, Wang Congjun, Wang He C, Wahid Kareem A, Joshi Sonali, Gerafian Parshan, West Natalie, Kaffey Zaphanlene, Mirbahaeddin Sarah, Curiel Jaqueline, Acharya Samrina, Shekha Amal, Oderinde Praise, Ali Alaa M S, Hope Andrew, Watson Erin, Wesson-Aponte Ruth, Frank Steven J, Barbon Carly E A, Brock Kristy K, Chambers Mark S, Walji Muhammad, Hutcheson Katherine A, Lai Stephen Y, Fuller Clifton D, Naser Mohamed A, Moreno Amy C, OPC-SURVIVOR Program, MD Anderson Head and Neck Cancer Symptom Working Group
Аннотация
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.