An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. (August 2019)

• Record Type: Journal Article
• Authors: Souza, Johnatan Carvalho; Bandeira Diniz, João Otávio; Ferreira, Jonnison Lima; França da Silva, Giovanni Lucca; Corrêa Silva, Aristófanes; de Paiva, Anselmo Cardoso
• Is Part Of: Computer methods and programs in biomedicine. Volume 177(2019)
• Page Start: 285
• Page End: 296
• Publication Date: 2019-08
• Subjects: Lung segmentation -- Lung reconstruction -- Chest x-ray -- Convolutional neural networks; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.06.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This work investigates a computational method for automatic lung segmentation. This method uses a database of chest X-Ray from the Montgomery County's Tuberculosis Control Program. The proposed method addresses the problem of dense abnormalities in chest X-Ray images to reconstruct the segmentation. The method uses two deep convolutional neural networks to perform lung segmentation in chest X-Ray images. The method achieved 97.54% of sensitivity, 96.79% of specificity, 96.79% accuracy, and 94% of dice index on lung segmentation in chest X-Ray images. Abstract: Background and Objective: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the automatic segmentation of the lung field. One of the main challenges inherent to this task is to include in the segmentation the lung regions overlapped by dense abnormalities, also known as opacities, which can be caused by diseases such as tuberculosis and pneumonia. This specific task is difficult because opacities frequently reach high intensity values which can be incorrectly interpreted by an automatic method as the lung boundary, and as a consequence, this creates a challenge in the segmentation process, because the chances of incomplete segmentations are increased considerably. The purpose of this work is to propose a method for automatic segmentation of lungs in CXR that addresses this problem by reconstructing the lung regions "lost" due to pulmonary abnormalities. Methods: The proposed method, which features two deep convolutional neural network models, consists of four steps main steps: (1) image acquisition, (2) initial segmentation, (3) reconstruction and (4) final segmentation. Results: The proposed method was experimented on 138 Chest X-ray images from Montgomery County's Tuberculosis Control Program, and has achieved as best result an average sensitivity of 97.54%, an average specificity of 96.79%, an average accuracy of 96.97%, an average Dice coefficient of 94%, and an average Jaccard index of 88.07%. Conclusions: We demonstrate in our lung segmentation method that the problem of dense abnormalities in Chest X-rays can be efficiently addressed by performing a reconstruction step based on a deep convolutional neural network model.