Lung segmentation in chest radiographs using fully convolutional networks

Authors : Rahul HOODA, Ajay MITTAL, Sanjeev SOFAT

Pages : 710-722

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Automated segmentation of medical images that aims at extracting anatomical boundaries is a fundamental step in any computer-aided diagnosis insert ignore into journalissuearticles values(CAD); system. Chest radiographic CAD systems, which are used to detect pulmonary diseases, first segment the lung field to precisely define the region-of-interest from which radiographic patterns are sought. In this paper, a deep learning-based method for segmenting lung fields from chest radiographs has been proposed. Several modifications in the fully convolutional network, which is used for segmenting natural images to date, have been attempted and evaluated to finally evolve a network fine-tuned for segmenting lung fields. The testing accuracy and overlap of the evolved network are 98.75 % and 96.10 %, respectively, which exceeds the state-of-the-art results.
Keywords : Chest X rays, deep learning, lung Segmentation, medical imaging