Fully convolutional neural network for lungs segmentation from chest X-rays

Abstract

Deep neural networks have entirely dominated the machine vision space in the past few years due to their astonishing human comparable performance. This paper applies power of such network to segment out lungs from chest x-rays, which is a crucial step in any computer aided diagnostic (CAD) system design. A fully convolutional network was used to extract lungs region from the x-rays. Post processing was done to fill holes, separate left and right lung from each other and remove unwanted objects that appeared in few cases. The process was repeated ten times, with random split of data into a 60:40 ratio as training and testing sets respectively, to calculate the average accuracy. The methodology was tested on three datasets: Japanese Society of Radiological Technology (JSRT), Montgomery County (MC), and a local dataset that achieved average accuracy of 97.1%, 97.7% & 94.2% respectively. The results proved that the proposed methodology is efficient enough and can be generalized for other such segmentation problems in medical imaging domain. Keywords: Chest radiography, Computer aided diagnostic system, Deep neural networks, Fully convolutional network, Lungs segmentation