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.v2
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.v2
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.v2
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.v2
Abstract
Teeth segmentation plays a pivotal role in dental diagnosis, treatment, planning, and the development of computer-aided dental systems. It enables precise identification and analysis of dental structures, aiding in detecting dental abnormalities, measuring tooth dimensions, and assessing oral health conditions. Accurate teeth segmentation also facilitates the automation of dental workflows, leading to improved efficiency and reduced human error. Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches developed or enhanced. 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 architecture, as previously used in [1], applied to 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 to develop automated systems to 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.
Received:
4 July 2023
Commenter:
Ali Ahmed Mahran
Commenter's Conflict of Interests:
Author
Comment: We have made several additions to the paper, including the inclusion of demonstrative figures and comparisons between different attention modules. Furthermore, we have trained a new model to enhance our analysis and results.
Commenter: Ali Ahmed Mahran
Commenter's Conflict of Interests: Author