Deep learning model for automated segmentation of sphenoid sinus and middle skull base structures in CBCT volumes using nnU-Net v2

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dc.authorid0000-0001-9677-8690
dc.authorid0000-0002-1014-4417
dc.contributor.authorGulsen, Ibrahim Tevfik
dc.contributor.authorKuran, Alican
dc.contributor.authorEvli, Cengiz
dc.contributor.authorBaydar, Oguzhan
dc.contributor.authorBasar, Kevser Dinc
dc.contributor.authorBilgir, Elif
dc.contributor.authorCelik, Ozer
dc.date.accessioned2026-01-24T12:30:59Z
dc.date.available2026-01-24T12:30:59Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractObjectiveThe purpose of this study is the development of a deep learning model based on nnU-Net v2 for the automated segmentation of sphenoid sinus and middle skull base anatomic structures in cone-beam computed tomography (CBCT) volumes, followed by an evaluation of the model's performance.Material and methodsIn this retrospective study, the sphenoid sinus and surrounding anatomical structures in 99 CBCT scans were annotated using web-based labeling software. Model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.01 for 1000 epochs. The performance of the model in automatically segmenting these anatomical structures in CBCT scans was evaluated using a series of metrics, including accuracy, precision, recall, dice coefficient (DC), 95% Hausdorff distance (95% HD), intersection on union (IoU), and AUC.ResultsThe developed deep learning model demonstrated a high level of success in segmenting sphenoid sinus, foramen rotundum, and Vidian canal. Upon evaluation of the DC values, it was observed that the model demonstrated the highest degree of ability to segment the sphenoid sinus, with a DC value of 0.96.ConclusionThe nnU-Net v2-based deep learning model achieved high segmentation performance for the sphenoid sinus, foramen rotundum, and Vidian canal within the middle skull base, with the highest DC observed for the sphenoid sinus (DC: 0.96). However, the model demonstrated limited performance in segmenting other foramina of the middle skull base, indicating the need for further optimization for these structures.
dc.identifier.doi10.1007/s11282-025-00848-9
dc.identifier.issn0911-6028
dc.identifier.issn1613-9674
dc.identifier.pmid40748555
dc.identifier.scopus2-s2.0-105012362951
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s11282-025-00848-9
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5559
dc.identifier.wosWOS:001541719500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofOral Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectDeep learning
dc.subjectArtificial intelligence
dc.subjectSphenoid sinus
dc.subjectMiddle skull base
dc.titleDeep learning model for automated segmentation of sphenoid sinus and middle skull base structures in CBCT volumes using nnU-Net v2
dc.typeArticle

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