Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q1
Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model
European Archives of Oto Rhino Laryngology · Kasım 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
Doç. Dr. 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
Doç. Dr. MURAT KÖKLÜ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Radiological classification of the infraorbital canal and correlation with variants of neighboring structures
2015 ISSN: 0937-4477 SCI-Expanded
Prof. Dr. ALAADDİN NAYMAN →
Gender differences in polysomnographic findings in Turkish patients with obstructive sleep apnea syndrome
2008 ISSN: 0937-4477 SCI-Expanded
Prof. Dr. METE KAAN BOZKURT →
The use of cyanoacrylates for wound closure in head and neck surgery
2008 ISSN: 0937-4477 SCI-Expanded
Prof. Dr. METE KAAN BOZKURT →
Bilateral external auditory canal squamous cell carcinoma a case report
2007 ISSN: 0937-4477 SCI-Expanded
Doç. Dr. ÇAĞDAŞ ELSÜRER →
First branchial cleft fistula presenting with external opening on earlobe
2006 ISSN: 0937-4477 SCI-Expanded
Doç. Dr. ÇAĞDAŞ ELSÜRER →
Ceruminous adenoma mimicking furunculosis in the external auditory canal
2007 ISSN: 0937-4477 SCI-Expanded
Doç. Dr. ÇAĞDAŞ ELSÜRER →
The effect of the presence of the accessory maxillary ostium on the maxillary sinus
2016 ISSN: 0937-4477 SCI-Expanded
Prof. Dr. ALAADDİN NAYMAN →
The effect of the presence of the accessory maxillary ostium on the maxillary sinus
2016 ISSN: 0937-4477 SCI-Expanded
Prof. Dr. ZELİHA FAZLIOĞULLARI →
The relationship between the findings of vestibular evoked myogenic potentials and severity of obstructive sleep apnea syndrome
2019 ISSN: 0937-4477 SCI-Expanded Q2
Prof. Dr. METE KAAN BOZKURT →
Classification and volumetric study of the sphenoid sinus on MDCT images
2019 ISSN: 0937-4477 SCI-Expanded
Prof. Dr. NADİRE ÜNVER DOĞAN →
The relationship between the findings of vestibular evoked myogenic potentials and severity of obstructive sleep apnea syndrome
2019 ISSN: 0937-4477 SCI-Expanded Q2
Doç. Dr. AHMET HAKAN EKMEKCİ →
The role of regulatory T cells in allergic rhinitis and their correlationwith IL‑10, IL‑17 and neopterin levels in serum and nasal lavage fluid
2020 ISSN: 0937-4477 SCI-Expanded Q1
Prof. Dr. ALİ ÜNLÜ →
The role of regulatory T cells in allergic rhinitis and their correlation with IL-10, IL-17 and neopterin levels in serum and nasal lavage fluid
2020 ISSN: 0937-4477 SCI-Expanded
Doç. Dr. HÜLYA ÖZDEMİR →
Auditory brainstem response in unilateral tinnitus patients: does symmetrical hearing thresholds and within-subject comparison affect responses?
2022 ISSN: 0937-4477 SCI-Expanded
Dr. Öğr. Üyesi BÜŞRA KAYNAKOĞLU →
The relationship between the findings of vestibular evoked myogenic potentials and severity of obstructive sleep apnea syndrome
2020 ISSN: 0937-4477 SCI
Dr. Öğr. Üyesi MUSLU KAZIM KÖREZ →
Classification and volumetric study of the sphenoid sinus on MDCT images
2019 ISSN: 0937-4477 SCI
Prof. Dr. NADİRE ÜNVER DOĞAN →
Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model
2024 ISSN: 0937-4477 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Evaluation of nasal mucociliary clearance in patients with psoriasis
2024 ISSN: 0937-4477 SCI-Expanded
Doç. Dr. CAHİT YAVUZ →
Makale Bilgileri
ISSN09374477
Yayın TarihiKasım 2024
Cilt / Sayfa281 · 6111-6121
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
Son Atıflar
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Scimago Dergi (ISSN Eşleşmesi)
European Archives of Oto-Rhino-Laryngology
Q1
SJR Skoru0,787
H-Index102
YayıncıSpringer Science and Business Media Deutschland GmbH
ÜlkeGermany
Otorhinolaryngology (Q1)
Medicine (miscellaneous) (Q2)
Metrikler
17
Atıf
4
Yazar
3
Anahtar Kelime