Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model
European Archives of Oto-Rhino-Laryngology · Ocak 2024
YÖKSİS Kayıtları
Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model
European Archives of Oto-Rhino-Laryngology · 2024 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model
European Archives of Oto-Rhino-Laryngology · 2024 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiEuropean Archives of Oto-Rhino-Laryngology
Yayın TarihiOcak 2024
Scopus ID2-s2.0-85200123218
Erişim🔓 Açık Erişim
Özet
Background: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully. Methods: In the study, axial CBCT images of 100 patients (200 maxillary sinuses) were used. These images were marked to identify the maxillary sinus walls. The marked regions are masked for use in the maxillary sinus segmentation model. U-Net, one of the deep learning methods, was used for segmentation. The training process was carried out for 10 epochs and 100 iterations per epoch. The epoch and iteration numbers in which the model showed maximum success were determined using the early stopping method. Results: After the segmentation operations performed with the U-Net model trained using CBCT images, both visual and numerical results were obtained. In order to measure the performance of the U-Net model, IoU (Intersection over Union) and F1 Score metrics were used. As a result of the tests of the model, the IoU value was found to be 0.9275 and the F1 Score value was 0.9784. Conclusion: The U-Net model has shown high success in maxillary sinus segmentation. In this way, fast and highly accurate evaluations are possible, saving time by reducing the workload of clinicians and eliminating subjective errors.
Yazarlar (4)
1
Busra Ozturk
ORCID: 0000-0001-6182-8758
2
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
3
Murat Koklu
ORCID: 0000-0002-2737-2360
4
Melek Tassoker
ORCID: 0000-0003-2062-5713
Anahtar Kelimeler
Cone beam computed tomography
Deep learning
Maxillary sinus
Kurumlar
Necmettin Erbakan Üniversitesi
Meram Turkey
Selçuk Üniversitesi
Selçuklu Turkey