Quick and accurate selection of hand images among radiographs from various body parts using deep learning.
Quick and accurate selection of hand images among radiographs from various body parts using deep learning.
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DOI：
10.3233/xst-200694
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发表时间：
2020-09
影响因子：
3
通讯作者：
K. Fujiwara;Fang Wanxuan;T. Okino;Kenneth Sutherland;A. Furusaki;A. Sagawa;T. Kamishima
中科院分区：
文献类型：
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作者：
K. Fujiwara;Fang Wanxuan;T. Okino;Kenneth Sutherland;A. Furusaki;A. Sagawa;T. Kamishima
虽然类风湿性关节炎（RA）会导致关节软骨破坏，但早期治疗可显著改善症状并延缓进展。重要的是要发现细微的损伤，以便早期诊断。最近的软件程序与传统的人类评分方法在RA放射学进展的可检测性方面具有可比性。因此，在各种身体部位的放射线图像中自动且准确地选择相关图像（例如，手图像）对于大规模的系列分析是必要的。目的在这项研究中，我们研究了深度学习是否可以从图像存档和通信系统（PACS）检索的大量存储图像中选择目标图像，包括患者的各种身体部位。方法选取1,047幅包含人体各部位的X线图像，分为训练组841幅和测试组206幅。训练图像被增强并用于训练由4个卷积层、2个池化层和2个全连接层组成的卷积神经网络（CNN）。训练后，我们创建了软件来对测试图像进行分类，并检查了准确性。结果单手和双手的图像提取准确率分别为0.952和0.979。此外，所有206个测试图像都被完美地分为单侧手，双手，和其他。结论：深度学习有望有效地自动选择RA患者的目标X射线图像。
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.