1. Development of retake support system for lateral knee radiographs by using deep convolutional neural network
- Author
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T. Ishida, K. Yamamoto, Y. Ohta, Y. Enchi, T. Kobayashi, and H. Matsuzawa
- Subjects
musculoskeletal diseases ,Computer science ,Radiography ,Computed tomography ,Knee Joint ,Convolutional neural network ,Imaging phantom ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Projection (set theory) ,medicine.diagnostic_test ,Phantoms, Imaging ,business.industry ,Deep learning ,030220 oncology & carcinogenesis ,Support system ,Neural Networks, Computer ,Artificial intelligence ,Tomography, X-Ray Computed ,business - Abstract
Introduction Lateral radiography of the knee joint is frequently performed; however, the retake rate is high owing to positioning errors. Therefore, in this study, to reduce the required number and time of image retakes, we developed a system that can classify the tilting directions of lateral knee radiographs and evaluated the accuracy of the proposed method. Methods Using our system, the tilting directions of a lateral knee radiographs were classified into four direction categories. The system was developed by training the DCNN based on 50 cases of Raysum images and tested on three types test dataset; ten more cases of Raysum images, one case of flexed knee joint phantom images and 14 rejected knee joint radiographs. To train a deep convolutional neural network (DCNN), we employed Raysum images created via three-dimensional (3D) X-ray computed tomography (CT); 11 520 Raysum images were created from 60 cases of 3D CT data by changing the projection angles. Thereby, we obtained pseudo images attached with correct labels that are essential for training. Results The overall accuracy on each test dataset was 88.5 ± 7.0% (mean ± standard deviation), 81.4 ± 11.2%, and 73.3 ± 9.2%. The larger the tilting degree of the knee joint, the higher the classification accuracy. Conclusion DCNN could classify the tilting directions of a knee joint from lateral knee radiographs. Using Raysum images made it possible to facilitate creating dataset for training DCNN. The possibility was indicated for using support system of lateral knee radiographs. Implications for practice The system may also reduce the burden on patients and increase the work efficiency of radiological technologists.
- Published
- 2021