Version 1
: Received: 20 June 2023 / Approved: 21 June 2023 / Online: 21 June 2023 (03:30:57 CEST)
Version 2
: Received: 2 July 2023 / Approved: 3 July 2023 / Online: 4 July 2023 (02:37:05 CEST)
How to cite:
Helmi Mahran, A.M.; Hussein, W.; Saber, S.E.D.M. Automatic Teeth Segmentation Using Attention U-Net. Preprints2023, 2023061468. https://doi.org/10.20944/preprints202306.1468.v1
Helmi Mahran, A.M.; Hussein, W.; Saber, S.E.D.M. Automatic Teeth Segmentation Using Attention U-Net. Preprints 2023, 2023061468. https://doi.org/10.20944/preprints202306.1468.v1
Helmi Mahran, A.M., Hussein, W., & Saber, S.E.D.M. (2023). Automatic Teeth Segmentation Using Attention U-Net. Preprints. https://doi.org/10.20944/preprints202306.1468.v1
Chicago/Turabian Style
Helmi Mahran, A.M., Walid Hussein and Shehab El Din Mohammed Saber. 2023 "Automatic Teeth Segmentation Using Attention U-Net" Preprints. https://doi.org/10.20944/preprints202306.1468.v1
Abstract
Dental radiography plays a crucial role in clinical diagnosis, treatment, and prognosis. In recent years, researchers have explored cutting-edge technologies to develop automated systems that can analyze radiographic imagery and support medical practitioners. The field of Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches being developed or improved upon. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net archi-tecture applied to the task of teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts in developing automated systems that can assist dental professionals in their clinical practice.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
