Accurate segmentation of lung fields on chest radiographs using deep convolutional networks

Mohammad R. Arbabshirani; Ahmed H. Dallal; Chirag Agarwal; Aalpan Patel; Gregory Moore

doi:10.1117/12.2254526

24 February 2017 Accurate segmentation of lung fields on chest radiographs using deep convolutional networks

Mohammad R. Arbabshirani, Ahmed H. Dallal, Chirag Agarwal, Aalpan Patel, Gregory Moore

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 1013305 (2017) https://doi.org/10.1117/12.2254526
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Accurate segmentation of lung fields on chest radiographs is the primary step for computer-aided detection of various conditions such as lung cancer and tuberculosis. The size, shape and texture of lung fields are key parameters for chest X-ray (CXR) based lung disease diagnosis in which the lung field segmentation is a significant primary step. Although many methods have been proposed for this problem, lung field segmentation remains as a challenge. In recent years, deep learning has shown state of the art performance in many visual tasks such as object detection, image classification and semantic image segmentation. In this study, we propose a deep convolutional neural network (CNN) framework for segmentation of lung fields. The algorithm was developed and tested on 167 clinical posterior-anterior (PA) CXR images collected retrospectively from picture archiving and communication system (PACS) of Geisinger Health System. The proposed multi-scale network is composed of five convolutional and two fully connected layers. The framework achieved IOU (intersection over union) of 0.96 on the testing dataset as compared to manual segmentation. The suggested framework outperforms state of the art registration-based segmentation by a significant margin. To our knowledge, this is the first deep learning based study of lung field segmentation on CXR images developed on a heterogeneous clinical dataset. The results suggest that convolutional neural networks could be employed reliably for lung field segmentation.

Conference Presentation

Citation Download Citation

Mohammad R. Arbabshirani, Ahmed H. Dallal, Chirag Agarwal, Aalpan Patel, and Gregory Moore "Accurate segmentation of lung fields on chest radiographs using deep convolutional networks", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013305 (24 February 2017); https://doi.org/10.1117/12.2254526

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available