Computer-Aided Diagnosis

Fully automated quantitative cephalometry using convolutional neural networks

[+] Author Affiliations
Sercan Ö. Arık

Baidu USA, 1195 Bordeaux Drive, Sunnyvale, California 94089, United States

Bulat Ibragimov, Lei Xing

Stanford University, Department of Radiation Oncology, School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305, United States

J. Med. Imag. 4(1), 014501 (Jan 06, 2017). doi:10.1117/1.JMI.4.1.014501
History: Received September 12, 2016; Accepted December 12, 2016
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Abstract.  Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy (1% to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy (76% average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Sercan Ö. Arık ; Bulat Ibragimov and Lei Xing
"Fully automated quantitative cephalometry using convolutional neural networks", J. Med. Imag. 4(1), 014501 (Jan 06, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.1.014501


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