Impacted Tooth Detection and Segmentation Based on Deep Convolutional Neural Network in Panoramic Dental Images

Year 2023, , 713 - 724, 18.10.2023

Meryem Durmuş , Burhan Ergen , Adalet Çelebi , Muammer Türkoğlu

https://doi.org/10.21605/cukurovaumfd.1377733

Cited By: 2

Abstract

Impacted tooth detection is an important step in dental practice and an accurate fixation process is of great importance in treatment planning and diagnosis. Considering the limitations and error probabilities of traditional methods, the utilization of artificial intelligence-based approaches like deep learning models is becoming more common. In this study, the performance of deep learning models was evaluated for the detection and segmentation of impacted teeth in panoramic dental images. The performance of seven different models (VGG16-Unet, VGG19-Unet, MobileNetV2, Unet-v1, Unet-v2, Unet-v3 and Unet-v4) was evaluated. The AUC (area under the curve) value of the VGG16-Unet model was found to be higher with 94.87% compared to the other models. This study contributes to the advancement of more accurate and sensitive segmentation methods in the field of dentistry and supports more reliable results in tooth detection and treatment planning processes.

Keywords

Unet, Deep learning, Impacted tooth, Panoramic dental images

Panoramik Diş Görüntülerinde Derin Evrişimsel Sinir Ağına Dayalı Gömülü Diş Tespiti ve Segmentasyonu

Abstract

Gömülü diş tespiti, diş hekimliği uygulamalarında önemli bir adımdır ve doğru bir tespit süreci, tedavi planlaması ve teşhislerde büyük önem taşır. Geleneksel yöntemlerin sınırlamaları ve hata olasılıkları göz önüne alındığında, derin öğrenme modelleri gibi yapay zekâ temelli yaklaşımların kullanılması giderek daha yaygın hale gelmektedir. Bu çalışmada panoramik gömülü diş görüntülerinde derin öğrenme modellerinin performansı incelenmiştir Yedi farklı modelin (VGG16-Unet, VGG19-Unet, MobileNetV2, Unet-v1, Unet-v2, Unet-v3 ve Unet-v4) performansı değerlendirilmiştir. VGG16-Unet modelinin AUC (eğri altındaki alan) değeri %94.87 ile diğer modellere kıyasla daha yüksek bulunmuştur. Bu çalışma, diş hekimliği alanında daha doğru ve hassas segmentasyon yöntemleri geliştirilmesine katkı sağlayarak, diş tespiti ve tedavi planlaması süreçlerinde daha güvenilir sonuçlar elde edilmesini desteklemektedir.

Keywords

Unet, Derin öğrenme, Gömülü diş, Panoramik diş görüntüleri

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