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Segmentation of Teeth in Panoramic X-ray Image Using U-net Algorithm

Publisher: IEEE

Abstract:

The segmentation of teeth in panoramic X-ray images is a critical task in dental imaging analysis. This study proposes the application of the U-Net algorithm, a convoluti...View more

Abstract:

The segmentation of teeth in panoramic X-ray images is a critical task in dental imaging analysis. This study proposes the application of the U-Net algorithm, a convolutional neural network architecture renowned for its prowess in biomedical image segmentation, to identify and delineate teeth structures within these images precisely. The process involves several key stages. Initially, a dataset comprising panoramic X-ray images alongside corresponding annotated masks indicating tooth locations is assembled. The U-Net architecture, featuring an encoder-decoder structure with skip connections, is employed to facilitate robust feature extraction and localization of teeth. Data preprocessing techniques, including normalization and augmentation, are applied to enhance the dataset’s diversity and prepare it for effective model training. The dataset is partitioned into training, validation, and testing subsets, ensuring proper evaluation and validation of the trained model. During training, a suitable loss function, such as the Dice coefficient or cross-entropy loss, is utilized to optimize the U-Net model parameters. Additionally, hyperparameter tuning and rigorous validation processes are conducted to enhance the model’s segmentation accuracy and prevent overfitting. Upon successful training, the model’s performance is evaluated using various metrics like Intersection over Union (IoU), Dice coefficient, and visual inspection of segmented masks overlaid on X-ray images. Finally, the trained U-Net model is deployed for inference on new panoramic X-ray images, providing precise segmentation of teeth structures. This proposed methodology leverages the robustness and accuracy of the U-Net algorithm, offering a promising approach for automated and accurate teeth segmentation in panoramic X-ray images, thereby assisting dental professionals in diagnosis and treatment planning. In addition to this, we aim to improve the accuracy and efficiency of dental diagnosis, treatment plan...
Date of Conference: 03-04 May 2024
Date Added to IEEE Xplore: 02 July 2024
ISBN Information:
Publisher: IEEE
Conference Location: Vellore, India

I. Introduction

In this study, we explore the utilization of the U-Net algorithm, a powerful convolutional neural network architecture, for the precise segmentation of teeth in panoramic X-ray images. The U-Net architecture, characterized by its encoder-decoder design with skip connections, has demonstrated remarkable performance in biomedical image segmentation tasks. The primary objective of this research is to develop an automated and accurate method for teeth segmentation, aiming to streamline the workflow of dental professionals and improve diagnostic accuracy [1]. By leveraging deep learning techniques and the U-Net architecture, we aim to create a model capable of efficiently identifying and outlining individual teeth structures within panoramic X-ray images. This investigation involves assembling a comprehensive dataset of panoramic X-ray images, each paired with meticulously annotated masks indicating the precise locations of teeth. Leveraging this dataset, the U-Net model will be trained to learn the intricate patterns and features associated with teeth structures in X-ray images. Key aspects of this research include data preprocessing techniques, model architecture design, optimization strategies, and rigorous evaluation methodologies. We will explore various metrics, such as Intersection over Union (IoU), Dice coefficient, and visual assessments, to quantify the accuracy and robustness of the proposed U-Net-based segmentation approach. The ultimate goal of this study is to develop a reliable and efficient automated teeth segmentation system that can seamlessly integrate into dental imaging workflows. Such a system has the potential to enhance the efficiency of dental professionals, improve diagnostic accuracy, and ultimately contribute to better patient care in the field of dentistry.

References

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