ResNet Tabanlı PSPNet Kullanarak Panoramik Görüntülerde Gömülü Diş Segmentasyon Analizi

Öz

Diş sağlığı, genel sağlık ve yaşam kalitesi üzerinde önemli bir etkiye sahiptir. Gömülü dişlerin segmentasyonu, diş hekimliğinde erken teşhis ve tedavi için kritik öneme sahip bir adımdır. Bu çalışmada, panoramik diş görüntülerindeki gömülü dişlerin doğru bir şekilde tanımlanması amacıyla derin öğrenme tekniklerinin kullanılması ele alınmıştır. Bu kapsamda, gömülü diş segmentasyonu için ResNet omurga ağına dayalı Piramit Sahne Ayrıştırma Ağı (PSPNet) geliştirilmiştir. Önerilen mimaride, önceden eğitilmiş ResNet omurga ağının ResNet18, ResNet34, ResNet50, ResNet101 ve ResNet152 versiyonları adapte edilmiştir. Bu çalışmada elde edilen bulgular göz önüne alındığında, diş görüntülerindeki segmentasyon ve tanıma süreçlerinde en yüksek başarıyı ResNet18 modeli ile elde edilmiştir (%92.09 F1 Skor, %93.88 Kesinlik, %90.39 Duyarlılık, %85.34 IoU Skor ve %96.89 Dice Katsayısı). Bu araştırma, panoramik diş görüntüleri üzerinde yapılan çalışmalar sonucunda, yetişkin hastalarda gömülü dişlerin başarıyla tespit edilme oranının yüksek olduğunu ortaya koymaktadır. Bu bulgular, yapay zekanın diş hekimleri için etkili bir yardımcı araç olabileceğini vurgulamakta ve sağlık sektöründeki yapay zeka gelişimine olan güveni artırmaktadır.

Anahtar Kelimeler

• Gömülü diş tespiti
• derin öğrenme
• ağız sağlığı
• ResNet
• PSPNet

Embedded Tooth Segmentation Analysis in Panoramic Images using ResNet Based PSPNet

Öz

Dental health has a significant impact on overall health and quality of life. Segmentation of impacted teeth is a critical step for early diagnosis and treatment in dentistry. In this study, we investigate the use of deep learning techniques to accurately identify impacted teeth in panoramic dental images. In this context, a Pyramid Scene Segmentation Network (PSPNet) based on ResNet backbone network is developed for embedded tooth segmentation. In the proposed architecture, ResNet18, ResNet34, ResNet50, ResNet101 and ResNet152 versions of the pre-trained ResNet backbone network are adapted. Considering the findings obtained in this study, the ResNet18 model achieved the highest success in segmentation and recognition processes in dental images (92.09% F1 Score, 93.88% Precision, 90.39% Recall, 85.34% IoU Score and 96.89% Dice Coefficient). This research reveals that the rate of successful detection of impacted teeth in adult patients is high as a result of studies on panoramic dental images. These findings emphasize that AI can be an effective tool for dentists and increase confidence in the development of AI in the healthcare sector.

Anahtar Kelimeler

• Impacted tooth detection
• deep learning
• oral health
• ResNet
• PSPNet

Kaynakça

1. Özkesici MY, Yılmaz S. Oral ve maksillofasiyal radyolojide yapay zekâ. Sağlık Bilimleri Dergisi. 2021; 30(3): 346-351.
2. Martins MV, Baptista L, Luís H, Assunção V, Araújo MR, Realinho V. Machine learning in x-ray diagnosis for oral health. A Review of Recent Progress, Computation, 2023; 11(6): 115.
3. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int, 2020; 51(3): 248-257.
4. Durmuş M, Ergen B, Çelebi A, Türkoğlu M. Panoramik diş görüntülerinde derin evrişimsel sinir ağına dayalı gömülü diş tespiti ve segmentasyonu. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 2023; 38(3): 713-724.
5. Kweon HHI, Lee JH, Youk TM, Lee BA, Kim YT. Panoramic radiography can be an effective diagnostic tool adjunctive to oral examinations in the national health checkup program. Journal of periodontal & implant science, 2018. 48(5): 317-325.
6. Schneider L, Arsiwala-Scheppach L, Krois J, Meyer-Lückel H, Bressem KK, Niehues SM, Schwendicke F. Benchmarking deep learning models for tooth structure segmentation. Journal of dental research, 2022; 101(11): 1343-1349.
7. Zhu J, Chen Z, Zhao J, Yu Y, Li X, Shi K, Zhang F, Yu F, Shi K, Sun Z, Lin N, Zheng, Y. Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study. BMC Oral Health, 2023; 23(1): 358.
8. Lee S, Oh SI, Jo J, Kang S, Shin Y, Park JW. Deep learning for early dental caries detection in bitewing radiographs. Scientific reports, 2021; 11(1): 16807.

9. Zadrożny Ł, Regulski P, Brus-Sawczuk K, Czajkowska M, Parkanyi L, Ganz S, Mijiritsky E. Artificial intelligence application in assessment of panoramic radiographs. Diagnostics, 2022; 12(1): 224.
10. Musri N, Christie B, Ichwan SJA, Cahyanto A. Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review. Imaging science in dentistry, 2021; 51(3): 237.
11. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific reports, 2019; 9(1): 3840.
12. Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 2020; 130(4): 464-469.
13. Imak A, Çelebi A, Polat O, Türkoğlu M, Şengür A. ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images. Oral Radiology, 2023; 1-15.
14. Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017; 2881-2890.
15. Yang C, Guo H. A method of image semantic segmentation based on pspnet. Mathematical Problems in Engineering, 2022.
16. Hossain MB, Iqbal SHS, Islam MM, Akhtar MN, Sarker, IH. Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images. Informatics in Medicine Unlocked, 2022; 30: 100916.
17. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; 770-778.
18. Haque IRI, Neubert J. Deep learning approaches to biomedical image segmentation. Informatics in Medicine Unlocked, 2020; 18: 100297.
19. Ahmed I, Ahmad M, Khan FA, Asif M. Comparison of deep-learning-based segmentation models: Using top view person images. IEEE Access, 2020; 8: 136361-136373.
20. Román JCM, Fretes VR, Adorno CG, Silva RG, Noguera JLV, Legal-Ayala H, Román JDM, Torres RDE, Facon J. Panoramic dental radiography image enhancement using multiscale mathematical morphology. Sensors, 2021; 21(9): 3110.
21. Abdi AH, Kasaei S, Mehdizadeh M. Automatic segmentation of mandible in panoramic x-ray. Journal of Medical Imaging, 2015; 2(4): 044003-044003.