Literature DB >> 26974042

A benchmark for comparison of dental radiography analysis algorithms.

Ching-Wei Wang1, Cheng-Ta Huang2, Jia-Hong Lee2, Chung-Hsing Li3, Sheng-Wei Chang4, Ming-Jhih Siao4, Tat-Ming Lai5, Bulat Ibragimov6, Tomaž Vrtovec6, Olaf Ronneberger7, Philipp Fischer7, Tim F Cootes8, Claudia Lindner8.   

Abstract

Dental radiography plays an important role in clinical diagnosis, treatment and surgery. In recent years, efforts have been made on developing computerized dental X-ray image analysis systems for clinical usages. A novel framework for objective evaluation of automatic dental radiography analysis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2015 Bitewing Radiography Caries Detection Challenge and Cephalometric X-ray Image Analysis Challenge. In this article, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the dental anatomy data repository of bitewing radiographs, the creation of the anatomical abnormality classification data repository of cephalometric radiographs, and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, seven automatic methods for analysing cephalometric X-ray image and two automatic methods for detecting bitewing radiography caries have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic dental radiography analysis is still a challenging and unsolved problem. The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/).
Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Keywords:  Anatomical segmentation and classification; Bitewing radiography analysis; Cephalometric tracing; Challenge and benchmark

Mesh:

Year:  2016        PMID: 26974042     DOI: 10.1016/j.media.2016.02.004

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  34 in total

1.  Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD.

Authors:  Ji-Hoon Park; Hye-Won Hwang; Jun-Ho Moon; Youngsung Yu; Hansuk Kim; Soo-Bok Her; Girish Srinivasan; Mohammed Noori A Aljanabi; Richard E Donatelli; Shin-Jae Lee
Journal:  Angle Orthod       Date:  2019-07-08       Impact factor: 2.079

2.  Automated identification of cephalometric landmarks: Part 2- Might it be better than human?

Authors:  Hye-Won Hwang; Ji-Hoon Park; Jun-Ho Moon; Youngsung Yu; Hansuk Kim; Soo-Bok Her; Girish Srinivasan; Mohammed Noori A Aljanabi; Richard E Donatelli; Shin-Jae Lee
Journal:  Angle Orthod       Date:  2019-07-22       Impact factor: 2.079

3.  Evaluation of automated cephalometric analysis based on the latest deep learning method.

Authors:  Hye-Won Hwang; Jun-Ho Moon; Min-Gyu Kim; Richard E Donatelli; Shin-Jae Lee
Journal:  Angle Orthod       Date:  2021-05-01       Impact factor: 2.079

4.  How much deep learning is enough for automatic identification to be reliable?

Authors:  Jun-Ho Moon; Hye-Won Hwang; Youngsung Yu; Min-Gyu Kim; Richard E Donatelli; Shin-Jae Lee
Journal:  Angle Orthod       Date:  2020-11-01       Impact factor: 2.079

5.  Fully automated quantitative cephalometry using convolutional neural networks.

Authors:  Sercan Ö Arık; Bulat Ibragimov; Lei Xing
Journal:  J Med Imaging (Bellingham)       Date:  2017-01-06

6.  CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Authors:  Shuai Wang; Kelei He; Dong Nie; Sihang Zhou; Yaozong Gao; Dinggang Shen
Journal:  Med Image Anal       Date:  2019-03-21       Impact factor: 8.545

7.  Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Authors:  Bulat Ibragimov; Lei Xing
Journal:  Med Phys       Date:  2017-02       Impact factor: 4.071

Review 8.  Deep Learning in Medical Image Analysis.

Authors:  Dinggang Shen; Guorong Wu; Heung-Il Suk
Journal:  Annu Rev Biomed Eng       Date:  2017-03-09       Impact factor: 9.590

9.  Tooth detection and numbering in panoramic radiographs using convolutional neural networks.

Authors:  Dmitry V Tuzoff; Lyudmila N Tuzova; Michael M Bornstein; Alexey S Krasnov; Max A Kharchenko; Sergey I Nikolenko; Mikhail M Sveshnikov; Georgiy B Bednenko
Journal:  Dentomaxillofac Radiol       Date:  2019-03-05       Impact factor: 2.419

10.  Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network.

Authors:  Odeuk Kwon; Tae-Hoon Yong; Se-Ryong Kang; Jo-Eun Kim; Kyung-Hoe Huh; Min-Suk Heo; Sam-Sun Lee; Soon-Chul Choi; Won-Jin Yi
Journal:  Dentomaxillofac Radiol       Date:  2020-07-03       Impact factor: 2.419

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