Artificial intelligence-based fully automatic 3D paranasal sinus segmentation

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dc.contributor.authorYigit, Meryem Kaygisiz
dc.contributor.authorPinarbasi, Alp
dc.contributor.authorEtoz, Meryem
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.date.accessioned2026-04-04T13:33:31Z
dc.date.available2026-04-04T13:33:31Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractObjectives Precise 3D segmentation of paranasal sinuses is essential for accurate diagnosis and treatment. This study aimed to develop a fully automated segmentation algorithm for the paranasal sinuses using the nnU-Net v2 architecture.Methods The nnU-Net v2-based segmentation algorithm was developed using Python 3.6.1 and the PyTorch library, and its performance was evaluated on a dataset of 97 cone beam CT (CBCT) scans. Ground truth annotations were manually generated by expert radiologists using the 3D Slicer software, employing a polygonal labelling technique across sagittal, coronal, and axial planes. Model performance was assessed using several quantitative metrics, including accuracy, Dice coefficient (DC), sensitivity, precision, Jaccard index, area under the curve (AUC), and 95% Hausdorff distance (95% HD).Results The nnU-Net v2-based algorithm demonstrated high segmentation performance across all paranasal sinuses. DC values were 0.94 for the frontal, 0.95 for the sphenoid, 0.97 for the maxillary, and 0.88 for the ethmoid sinuses. Accuracy scores exceeded 99% for all sinuses. The 95% HD values were 0.51 mm for both the frontal and maxillary sinuses, 0.85 mm for the sphenoid sinus, and 1.17 mm for the ethmoid sinus. Jaccard indices were 0.90, 0.91, 0.94, and 0.80, respectively.Conclusions This study highlights the high accuracy and precision of the nnU-Net v2-based CNN model in the fully automated segmentation of paranasal sinuses from CBCT images. The results suggest that the proposed model can significantly contribute to clinical decision-making processes, facilitating diagnostic and therapeutic procedures.
dc.description.sponsorshipErciyes University Scientific Research. Projects Coordination Unit [TDH-2023-13180]
dc.description.sponsorshipThis work has been supported by Erciyes University Scientific Research. Projects Coordination Unit under grant number: TDH-2023-13180.
dc.identifier.doi10.1093/dmfr/twaf057
dc.identifier.endpage72
dc.identifier.issn0250-832X
dc.identifier.issn1476-542X
dc.identifier.issue1
dc.identifier.orcid0000-0001-7222-0430
dc.identifier.orcid0000-0003-1192-4105
dc.identifier.orcid0009-0000-2619-1827
dc.identifier.orcid0000-0001-5036-9867
dc.identifier.orcid0000-0003-2552-0187
dc.identifier.pmid40711942
dc.identifier.scopus2-s2.0-105027290406
dc.identifier.scopusqualityQ1
dc.identifier.startpage61
dc.identifier.urihttps://doi.org/10.1093/dmfr/twaf057
dc.identifier.urihttps://hdl.handle.net/11616/109191
dc.identifier.volume55
dc.identifier.wosWOS:001555855100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofDentomaxillofacial Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectparanasal sinuses
dc.subjectartificial intelligence
dc.subjectsegmentation
dc.subjectdeep learning
dc.subjectnnU-Net
dc.titleArtificial intelligence-based fully automatic 3D paranasal sinus segmentation
dc.typeArticle

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