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Fully automated 3D segmentation of periapical lesions on CBCT images using nnU-Net v2

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dc.contributor.authorHasan Öksüzoğlu
dc.contributor.authorÖzgür Topuz
dc.contributor.authorElif Bilgir
dc.contributor.authorÖzer Çelik
dc.contributor.authorIbrahim Şevki Bayrakdar
dc.contributor.orcid0000-0002-8300-6963
dc.contributor.orcid0000-0001-9521-4682
dc.contributor.orcid0000-0002-4409-3101
dc.date.accessioned2026-05-20T10:24:18Z
dc.date.issued2026-03-30
dc.identifier.doi10.1186/s12880-026-02316-0
dc.identifier.issn1471-2342
dc.identifier.issue1
dc.identifier.openalexW7143370931
dc.identifier.urihttps://hdl.handle.net/11421/39921
dc.identifier.urihttps://doi.org/10.1186/s12880-026-02316-0
dc.identifier.volume26
dc.language.isoen
dc.relation.ispartofBMC Medical Imaging
dc.rightsopenAccess
dc.subjectSegmentation
dc.subjectSørensen–Dice coefficient
dc.subjectInterquartile range
dc.subjectCohen's kappa
dc.subjectStandard deviation
dc.subjectImage segmentation
dc.subjectHausdorff distance
dc.subjectCone beam computed tomography
dc.subject.sdg10
dc.titleFully automated 3D segmentation of periapical lesions on CBCT images using nnU-Net v2
dspace.entity.typePublication

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