Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Oxford Univ Press
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Objectives The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.Methods In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.Results F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.Conclusions Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.
Açıklama
Anahtar Kelimeler
artificial intelligence, computer modelling, deep learning, CBCT, maxillary sinus
Kaynak
Dentomaxillofacial Radiology
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
53
Sayı
4
