Lung segmentation in chest radiographs using fully convolutional networks

Authors

RAHUL HOODA
AJAY MITTAL
SANJEEV SOFAT

DOI

10.3906/elk-1710-157

Abstract

Automated segmentation of medical images that aims at extracting anatomical boundaries is a fundamental step in any computer-aided diagnosis (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

First Page

710

Last Page

722

Recommended Citation

HOODA, RAHUL; MITTAL, AJAY; and SOFAT, SANJEEV (2019) "Lung segmentation in chest radiographs using fully convolutional networks," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27: No. 2, Article 3. https://doi.org/10.3906/elk-1710-157
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss2/3