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Evaluation and comparison of anatomical landmark detection methods for cephalometric X-Ray images: a grand challenge

Wang, C.-W., Huang, C.-T., Hsieh, M.-C., Li, C. H., Chang, S.-W., Li, W.-C., Vandaele, R., Marée, R., Jodogne, S., Geurts, P., Chen, C., Zheng, G., Chu, C., Mirzaalian, H., Hamarneh, G., Vrtovec, T. and Ibragimov, B. (2015) Evaluation and comparison of anatomical landmark detection methods for cephalometric X-Ray images: a grand challenge. IEEE Transactions on Medical Imaging, 34 (9). pp. 1890-1900. ISSN 1558-254X

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To link to this item DOI: 10.1109/TMI.2015.2412951

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:No Reading authors. Back catalogue items
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:90723
Publisher:IEEE

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