Comparison of VT-based and CNN-based Models on Teeth Segmentation

Abstract

Semantic segmentation is a crucial task in computer vision with a wide array of applications across various fields, especially in medical imaging. One of the most important applications of semantic segmentation is in the field of dentistry, where teeth segmentation plays a significant role in diagnosing and treating oral health issues. Accurate segmentation of teeth in dental images is vital for detecting abnormalities, planning treatments, and monitoring the progress of dental procedures. In this paper, a comprehensive comparative analysis is presented, focusing on the use of Convolutional Neural Network (CNN)-based and Vision Transformer (VT)-based models for image segmentation within the context of dentistry. The paper presents a comparison of eight different models, contributing to the literature on dental image segmentation and showcasing practical applications in clinical dental settings. The research presented in this study uses several state-of-the-art segmentation models, namely U-Net, LinkNet, and Swin U-Net, along with different backbones to perform teeth segmentation on publicly available two datasets: one representing adults and the other children. The experiments were conducted to determine which models and backbones provided the best segmentation performance for each dataset. The study also emphasizes that the segmentation modeling process should be handled separately since the alignment of child and adult teeth is different. The U-Net model with the ResNet101 backbone achieved the best performance on the adults dataset, while for the children dataset, the U-Net model with the same ResNet101 backbone also demonstrated superior results. The highest Dice scores obtained were 0.9543 for the adults dataset and 0.9019 for the children dataset, indicating the effectiveness of these models in accurately segmenting teeth. The findings from this research demonstrate the potential of deep learning techniques in improving the accuracy and efficiency of dental diagnosis and treatment planning. Codes used throughout the study will be publicly available at https://github.com/FidanVural/Teeth-Segmentation-in- Panoramic-Radiography/tree/main

Keywords

• Convolutional Neural Networks
• Panoramic Radiography
• Semantic Segmentation
• Vision Transformer

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