A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

Abstract

Introduction: Dental segmentation in panoramic radiograph has become very relevant in dentistry, since it allows health professionals to carry out their assessments more clearly and helps them to define the best possible treatment plan for their patients. Objectives: In this work, a comparative study is carried out with four segmentation algorithms (U-Net, DCU-Net, DoubleU-Net and Nano-Net) that are prominent in the medical literature on segmentation and we evaluate their results with the current state of the art of dental segmentation in panoramic radiograph. Methods: These algorithms were tested with a dataset consisting of 1,500 images, considering experiment scenarios with and without augmentation data. Results: DoubleU-Net was the model that presented the best results among the analyzed models, reaching 96.591% accuracy and 92.886% Dice using the dataset with data augmentation. Another model that stood out was Nano-Net using the dataset without data augmentation; this model achieved results close to that of the literature with only 235 thousand trainable parameters, while the literature model (TSASNet) contains 78 million. Conclusions: The results obtained in this work are satisfactory and present paths for a better and more effective dental segmentation process.

Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis

“…Image augmentation produces a large number of image variations and increases data diversity. In line with previous studies, 12 22 23 image rotation was used for data augmentation in this study. Several techniques, such as horizontal inversion 10 12 23 and shifts of vertical alignment, brightness, sharpness, or contrast, can be applied for data augmentation in the image segmentation of panoramic radiographs.…”

“…In line with previous studies, 12 22 23 image rotation was used for data augmentation in this study. Several techniques, such as horizontal inversion 10 12 23 and shifts of vertical alignment, brightness, sharpness, or contrast, can be applied for data augmentation in the image segmentation of panoramic radiographs. 22 As presented in Figure 4 , augmentation was performed with Multi-Label U-Net and Mask R-CNN and significantly improved the dice coefficient and IoU score of both deep learning models.…”

“…Various U-Net models are effective for tooth segmentation in panoramic radiographs. 23 U-Net also shows acceptable to high accuracy for the detection of proximal caries on bitewing radiographs. 25 Although U-Net is a method for semantic segmentation, it has the potential for automatic periodontitis detection when combined with other classification techniques.…”

“…Because it divides the intersection by the union of the two areas, IoU has been widely used for quantifying the similarity between predicted and ground truth areas. 25 26 This metric has also been applied to assess a complete deep learning Mask R-CNN, 27 compare 4 segmentation algorithms (U-Net, DCU-Net, DoubleU-Net, and Nano-Net) for dental segmentation in panoramic radiographs, 23 and evaluate the ability of U-Net to detect caries lesions on bitewing radiographs. 25 …”

“…U-Net is a semantic segmentation model that employs convolutional layers using a symmetrical network architecture. 23 Its architecture is defined by the pattern of convolutional network layers aligned in a U shape and the use of skip connections between them. The encoder is the left part of the “U”, and the decoder is the right part.…”