Literature DB >> 25794388

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge.

Ching-Wei Wang, Cheng-Ta Huang, Meng-Che Hsieh, Chung-Hsing Li, Sheng-Wei Chang, Wei-Cheng Li, Rémy Vandaele, Raphaël Marée, Sébastien Jodogne, Pierre Geurts, Cheng Chen, Guoyan Zheng, Chengwen Chu, Hengameh Mirzaalian, Ghassan Hamarneh, Tomaz Vrtovec, Bulat Ibragimov.   

Abstract

Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

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Year:  2015        PMID: 25794388     DOI: 10.1109/TMI.2015.2412951

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  22 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.  Artificial intelligence in orthodontics : Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network.

Authors:  Felix Kunz; Angelika Stellzig-Eisenhauer; Florian Zeman; Julian Boldt
Journal:  J Orofac Orthop       Date:  2019-12-18       Impact factor: 1.938

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

8.  Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views.

Authors:  Bastian Bier; Florian Goldmann; Jan-Nico Zaech; Javad Fotouhi; Rachel Hegeman; Robert Grupp; Mehran Armand; Greg Osgood; Nassir Navab; Andreas Maier; Mathias Unberath
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-04-20       Impact factor: 2.924

9.  Dynamic 3-D MR Visualization and Detection of Upper Airway Obstruction During Sleep Using Region-Growing Segmentation.

Authors:  Ahsan Javed; Yoon-Chul Kim; Michael C K Khoo; Sally L Davidson Ward; Krishna S Nayak
Journal:  IEEE Trans Biomed Eng       Date:  2015-08-03       Impact factor: 4.538

10.  Automated landmarking for palatal shape analysis using geometric deep learning.

Authors:  Balder Croquet; Harold Matthews; Jules Mertens; Yi Fan; Nele Nauwelaers; Soha Mahdi; Hanne Hoskens; Ahmed El Sergani; Tianmin Xu; Dirk Vandermeulen; Michael Bronstein; Mary Marazita; Seth Weinberg; Peter Claes
Journal:  Orthod Craniofac Res       Date:  2021-07-21       Impact factor: 1.826

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