U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs.
Objectives: This study aimed to develop and evaluate a deep convolutional neural network (CNN) model for the automatic segmentation of foreign bodies and ghost images in panoramic radiographs (PRs), which can complicate diagnostic interpretation. Methods: A dataset of 11,226 PRs from four devices wa...
| Publicado en: | Oral Radiology Vol. 42; no. 2; pp. 333 - 341 |
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| Autores principales: | , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192344422&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192344422 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09116028 1P9 jtl: Oral Radiology issn: 09116028 maglogo: N pubinfo: dt: Apr2026 vid: 42 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 192344422 188017050 192344422 192344422 10.1007/s11282-025-00862-x 192344422 ppf: 333 ppct: 8 formats: tig: atl: U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs. aug: au: Çelebi, Elif Akkaya, Nurullah Ünsal, Gürkan affil: https://ror.org/00yze4d93 Department of Oral and Maxillofacial Radiology, School of Dental Medicine, Bahçeşehir University, Gayrettepe, Barbaros Boulevard No:153, Beşiktaş, 34357, Istanbul, Turkey sug: subj: Neural Networks (Computer) Models, Statistical Foreign Bodies Radiography Image Processing, Computer Assisted Diagnostic Imaging Radiography, Panoramic Image Interpretation, Computer Assisted Human Computer Simulation Retrospective Design Descriptive Statistics Data Analysis Software Magnetic Resonance Imaging Tomography, X-Ray Computed ab: Objectives: This study aimed to develop and evaluate a deep convolutional neural network (CNN) model for the automatic segmentation of foreign bodies and ghost images in panoramic radiographs (PRs), which can complicate diagnostic interpretation. Methods: A dataset of 11,226 PRs from four devices was annotated by two radiologists using the Computer Vision Annotation Tool. A U-Net-based CNN model was trained and evaluated using Intersection over Union (IoU), Dice coefficient, accuracy, precision, recall, and F1 score. Results: For foreign body segmentation, the model achieved validation Dice and IoU scores of 0.9439 and 0.9043, and test scores of 0.9657 and 0.9371. For ghost image segmentation, validation Dice and IoU were 0.8234 and 0.7388, with test scores of 0.8749 and 0.8145. Overall test accuracy exceeded 0.999. Conclusions: The AI model showed high accuracy in segmenting foreign bodies and ghost images in PRs, indicating its potential to assist radiologists. Further clinical validation is recommended. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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