27 February 2018 Localization of lung fields in HRCT images using a deep convolution neural network
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Lung field segmentation is a prerequisite step for the development of a computer-aided diagnosis system for interstitial lung diseases observed in chest HRCT images. Conventional methods of lung field segmentation rely on a large gray value contrast between lung fields and surrounding tissues. These methods fail on lung HRCT images with dense and diffused pathology. An efficient prepro- cessing could improve the accuracy of segmentation of pathological lung field in HRCT images. In this paper, a convolution neural network is used for localization of lung fields in HRCT images. The proposed method provides an optimal bounding box enclosing the lung fields irrespective of the presence of diffuse pathology. The performance of the proposed algorithm is validated on 330 lung HRCT images obtained from MedGift database on ZF and VGG networks. The model achieves a mean average precision of 0.94 with ZF net and a slightly better performance giving a mean average precision of 0.95 in case of VGG net.
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Abhishek Kumar, Abhishek Kumar, Sunita Agarwala, Sunita Agarwala, Ashis Kumar Dhara, Ashis Kumar Dhara, Sudipta Mukhopadhyay, Sudipta Mukhopadhyay, Debashis Nandi, Debashis Nandi, Mandeep Garg, Mandeep Garg, Niranjan Khandelwal, Niranjan Khandelwal, Naveen Kalra, Naveen Kalra, "Localization of lung fields in HRCT images using a deep convolution neural network", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057535 (27 February 2018); doi: 10.1117/12.2293503; https://doi.org/10.1117/12.2293503

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