Quick and accurate selection of hand images among radiographs from various body parts using deep learning
Autor: | Wanxuan Fang, Akira Sagawa, Tamotsu Kamishima, Kenneth Sutherland, Kohei Fujiwara, Akira Furusaki, Taichi Okino |
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Rok vydání: | 2020 |
Předmět: |
Computer science
Radiography Convolutional neural network 030218 nuclear medicine & medical imaging Arthritis Rheumatoid 03 medical and health sciences Deep Learning 0302 clinical medicine Picture archiving and communication system Software Humans Radiology Nuclear Medicine and imaging Electrical and Electronic Engineering Instrumentation Selection (genetic algorithm) Radiation Contextual image classification business.industry Deep learning Pattern recognition Hand Condensed Matter Physics Radiographic Image Interpretation Computer-Assisted Artificial intelligence Image extraction business 030217 neurology & neurosurgery |
Zdroj: | Journal of X-Ray Science and Technology. 28:1199-1206 |
ISSN: | 1095-9114 0895-3996 |
Popis: | BACKGROUND: Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs are comparable with the conventional human scoring method regarding detectability of the radiographic progression of RA. Thus, automatic and accurate selection of relevant images (e.g. hand images) among radiographic images of various body parts is necessary for serial analysis on a large scale. OBJECTIVE: In this study we examined whether deep learning can select target images from a large number of stored images retrieved from a picture archiving and communication system (PACS) including miscellaneous body parts of patients. METHODS: We selected 1,047 X-ray images including various body parts and divided them into two groups: 841 images for training and 206 images for testing. The training images were augmented and used to train a convolutional neural network (CNN) consisting of 4 convolution layers, 2 pooling layers and 2 fully connected layers. After training, we created software to classify the test images and examined the accuracy. RESULTS: The image extraction accuracy was 0.952 and 0.979 for unilateral hand and both hands, respectively. In addition, all 206 test images were perfectly classified into unilateral hand, both hands, and the others. CONCLUSIONS: Deep learning showed promise to enable efficiently automatic selection of target X-ray images of RA patients. |
Databáze: | OpenAIRE |
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