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KMID : 1137820170380060321
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의공학회지
2017 Volume.38 No. 6 p.321 ~ p.329
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Development of Bone Metastasis Detection Algorithm on Abdominal Computed Tomography Image using Pixel Wise Fully Convolutional Network
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Kim Joo-Young
Lee Si-Young
Kim Kyu-Ri
Cho Kyeong-Won
You Sung-Min
So Soon-Won
Park Eun-Kyoung
Cho Baek-Hwan
Choi Dong-Il
Park Hoon-Ki
Kim In-Young
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Abstract
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This paper presents a bone metastasis Detection algorithm on abdominal computed tomography images for early detection using fully convolutional neural networks. The images were taken from patients with various cancers (such as lung cancer, breast cancer, colorectal cancer, etc), and thus the locations of those lesions were varied. To overcome the lack of data, we augmented the data by adjusting the brightness of the images or flipping the images. Before the augmentation, when 70% of the whole data were used in the pre-test, we could obtain the pixel-wise sensitivity of 18.75%, the specificity of 99.97% on the average of test dataset. With the augmentation, we could obtain the sensitivity of 30.65%, the specificity of 99.96%. The increase in sensitivity shows that the augmentation was effective. In the result obtained by using the whole data, the sensitivity of 38.62%, the specificity of 99.94% and the accuracy of 99.81% in the pixel-wise. lesion-wise sensitivity is 88.89% while the false alarm per case is 0.5. The results of this study did not reach the level that could substitute for the clinician. However, it may be helpful for radiologists when it can be used as a screening tool.
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KEYWORD
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Bone metastasis, Deep learning, Cancer, Computed Tomography
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