Lung image Ssgmentation using deep learning methods and convolutional neural networks

Abstract

This paper presents results of the first, exploratory stage of research and developments on segmentation of lungs in X-Ray chest images (Chest Radiographs) using Deep Learning methods and Encoder-Decoder Convolutional Neural Networks (EDCNN). Computational experiments were conducted using GPU Nvidia TITAN X equipped with 3072 CUDA Cores and 12Gb of GDDR5 memory. Comparison of resultant segmentation accuracy with manual segmentation using Dice's score has revealed that the average accuracy achieves 0.962 with the minimum and maximum Dice's score values of 0.926, 0.974 respectively, and standard deviation of 0.008. The study was performed in the context of large-scale screening of population for lung and heart diseases as well as development of computational services for international portal on lung tuberculosis. The results obtained with this study allow concluding that ED-CNN networks may be considered as a promising tool for automatic lung segmentation in large-scale projects.