Comparative Analysis of GAN-based Segmentation Models on Dental Panoramic Radiography Dataset

Öz

Especially in criminal investigations, the identification of the victim is essential. The branch of forensic medicine that uses the method of identification from the teeth of the victims is called forensic odontology. In forensic odontology, physical information about the individual can be obtained from the bone and enamel structure of the teeth. Panoramic, periapical, and cephalometric imaging techniques are the most commonly used in the odontological identification of the individual. Forensic odontology is increasingly recognized for its essential role in personal identification during mass disasters, sexual assault cases, and child abuse investigations. Deep learning algorithms have recently successfully detected dental disorders such as caries, periodontal bone loss, and apical lesions. Generative adversarial networks (GAN) models have mainly achieved high segmentation performance in medical images. In this study, GAN models were designed and comparatively analyzed using U-Net, Volumetric convolutional neural network (V-Net), spatial and channel Squeeze-Excitation-based U-Net(scSEU-Net), Transformer-based U-Net (TransU-Net), and U-Net like pure Transformer (SwinU-Net) segmentation architectures which are widely used in the literature as generators. As a result of the comparative analyses, scSEU-Net-based GAN achieved the highest performance values with 0.8826 Thresholded Dice(DSC), 0.7901 Thresholded Intersection over Union (Thresh-IoU), 0.9805 Accuracy (ACC), 0.9268 Precision (PREC), and 0.9001 Recall (REC).

Anahtar Kelimeler

• Deep Learning
• Dental panoramic radiography segmentation
• Generative adversarial networks
• U-shaped Segmentation models
• V-Net.

Dental Panoramik Radyografi Veri Setinde GAN Tabanlı Segmentasyon Modellerinin Karşılaştırmalı Analizi

Öz

Özellikle ceza soruşturmalarında mağdurun kimliği esastır. Mağdurların dişlerinden kimlik tespiti yöntemini kullanan adli tıp dalına adli odontoloji denir. Adli odontolojide, bireye ait fiziksel bilgiler dişlerin kemik ve mine yapısından elde edilebilir. Bireyin odontolojik kimlik tespitinde en sık kullanılanlar panoramik, periapikal ve sefalometrik görüntüleme teknikleridir. Adli odontoloji, kitlesel felaketler, cinsel saldırı vakaları ve çocuk istismarı soruşturmaları sırasında kişisel kimlik tespitindeki temel rolüyle giderek daha fazla tanınmaktadır. Derin öğrenme algoritmaları son zamanlarda çürük, periodontal kemik kaybı ve apikal lezyonlar gibi diş bozukluklarını başarıyla tespit etmiştir. Üretken Çekişmeli Ağlar (GAN) modelleri çoğunlukla medikal görüntülerde yüksek segmentasyon performansı elde etmiştir. Bu çalışmada, literatürde üreteç olarak yaygın olarak kullanılan U-Net, Volumetrik Evrişimli Sinir Ağı (V-Net), Uzaysal ve Kanal Sıkıştırma-Uyartım Tabanlı U-Net (scSEU-Net), Transformatör Tabanlı U-Net (TransU-Net) ve U-Net benzeri Saf Transformatör (SwinU-Net) segmentasyon mimarileri kullanılarak GAN modelleri tasarlanmış ve karşılaştırmalı olarak analiz edilmiştir. Karşılaştırmalı analizler sonucunda scSEU-Net tabanlı GAN, 0,8826 Eşikli Zar (DSC), 0,7901 Eşikli Birleşim Kesişimi (Thresh-IoU), 0,9805 Doğruluk (ACC), 0,9268 Hassasiyet (PREC) ve 0,9001 Geri Çağırma (REC) değerleriyle en yüksek performans değerlerine ulaşmıştır.

Anahtar Kelimeler

• Anahtar kelimeler: Derin Öğrenme
• Dental panoramik radyografi segmentasyonu
• Üretken düşmanca ağlar
• U şeklinde segmentasyon modelleri
• V-Net.

Kaynakça

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