# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
###### Abstract  
**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 1500 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.  
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pattern [5]. Thus, the literature review carried out by [PERSON] et al. [4] revealed that there is still a need for an adequate method of segmenting dental radiographic images that can be used as a basis for the construction of specialized systems to aid in dental diagnosis.  
With advances in artificial intelligence (AI), such as machine learning and deep learning, computer-aided diagnostic systems have been widely used in various medical areas, such as automated image-based diagnosis to detect lung and brain lesions [6; 7; 8], breast cancer in mammography images [9], segmentation of brain lesions [10] and segmentation of pulmonary nodules of various densities [11], among others. In dentistry, these techniques have also been applied in various segments. For example, the authors [12] used a convolutional neural network (CNN) to diagnose dental caries in premolars, molars or both, using periapical radiographs. In [13], a CNN was used to detect apical lesions in panoramic-rays. In [14], deep learning was used to segment the mandibular canal using computed tomography volumes. While in [15], segmentation of teeth in panoramic radiographs was performed. Although the results of previous research on AI have been extremely promising, the studies are still preliminary.  
Starting from the idea that AI can aid dentists in identifying and segmenting teeth faster with the possibility of minimizing errors associated with human fatigue [3], this study aims to perform a comparison of segmentation of computational vision models and investigate their performance in segmenting panoramic radiograph images.  
This paper is divided into five sections. After the introduction (Section 1), Section 2 provides an overview of related work in the field of segmentation in panoramic radiograph images. Section 3 describes the materials and methods of the work, such as the dataset used, the proposed models, applied loss function and evaluation metrics. Section 4 describes the experiments and presents the results and discussion. Finally, in Section 5, conclusions and presentation of future work are provided.