Localization of lung fields in HRCT images using a deep convolution neural network

Abhishek Kumar; Sunita Agarwala; Ashis Kumar Dhara; Sudipta Mukhopadhyay; Debashis Nandi; Mandeep Garg; Niranjan Khandelwal; Naveen Kalra

doi:10.1117/12.2293503

27 February 2018 Localization of lung fields in HRCT images using a deep convolution neural network

Abhishek Kumar, Sunita Agarwala, Ashis Kumar Dhara, Sudipta Mukhopadhyay, Debashis Nandi, Mandeep Garg, Niranjan Khandelwal, Naveen Kalra

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057535 (2018) https://doi.org/10.1117/12.2293503
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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, Sunita Agarwala, Ashis Kumar Dhara, Sudipta Mukhopadhyay, Debashis Nandi, Mandeep Garg, Niranjan Khandelwal, and 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); https://doi.org/10.1117/12.2293503

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