Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.

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Altun O., Özen D., Duman Ş. B., Dedeoğlu N., Bayrakdar İ. Ş., Eşer G., ...Daha Fazla

BMC oral health, cilt.24, sa.1, ss.1208, 2024 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1186/s12903-024-04924-0
  • Dergi Adı: BMC oral health
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1208
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • İnönü Üniversitesi Adresli: Evet

Özet

Background

Maxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries.

Objective

The aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images.

Methods

Data set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features.

Results

Recall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation.

Conclusion

This study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.