Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network

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Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network

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Title: Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network
Author: Tu, Xiaoguang; Xie, Mei; Gao, Jingjing; Ma, Zheng; Chen, Daiqiang; Wang, Qingfeng; Finlayson, Samuel G.; Ou, Yangming; Cheng, Jie-Zhi

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Citation: Tu, Xiaoguang, Mei Xie, Jingjing Gao, Zheng Ma, Daiqiang Chen, Qingfeng Wang, Samuel G. Finlayson, Yangming Ou, and Jie-Zhi Cheng. 2017. “Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network.” Scientific Reports 7 (1): 8533. doi:10.1038/s41598-017-08040-8. http://dx.doi.org/10.1038/s41598-017-08040-8.
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Abstract: We present a computer-aided diagnosis system (CADx) for the automatic categorization of solid, part-solid and non-solid nodules in pulmonary computerized tomography images using a Convolutional Neural Network (CNN). Provided with only a two-dimensional region of interest (ROI) surrounding each nodule, our CNN automatically reasons from image context to discover informative computational features. As a result, no image segmentation processing is needed for further analysis of nodule attenuation, allowing our system to avoid potential errors caused by inaccurate image processing. We implemented two computerized texture analysis schemes, classification and regression, to automatically categorize solid, part-solid and non-solid nodules in CT scans, with hierarchical features in each case learned directly by the CNN model. To show the effectiveness of our CNN-based CADx, an established method based on histogram analysis (HIST) was implemented for comparison. The experimental results show significant performance improvement by the CNN model over HIST in both classification and regression tasks, yielding nodule classification and rating performance concordant with those of practicing radiologists. Adoption of CNN-based CADx systems may reduce the inter-observer variation among screening radiologists and provide a quantitative reference for further nodule analysis.
Published Version: doi:10.1038/s41598-017-08040-8
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581338/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34492080
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