Contextual convolutional neural networks for lung nodule classification using Gaussian-weighted average image patches

Haeil Lee; Hansang Lee; Minseok Park; Junmo Kim

doi:10.1117/12.2253978

3 March 2017 Contextual convolutional neural networks for lung nodule classification using Gaussian-weighted average image patches

Haeil Lee, Hansang Lee, Minseok Park, Junmo Kim

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013423 (2017) https://doi.org/10.1117/12.2253978
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Lung cancer is the most common cause of cancer-related death. To diagnose lung cancers in early stages, numerous studies and approaches have been developed for cancer screening with computed tomography (CT) imaging. In recent years, convolutional neural networks (CNN) have become one of the most common and reliable techniques in computer aided detection (CADe) and diagnosis (CADx) by achieving state-of-the-art-level performances for various tasks. In this study, we propose a CNN classification system for false positive reduction of initially detected lung nodule candidates. First, image patches of lung nodule candidates are extracted from CT scans to train a CNN classifier. To reflect the volumetric contextual information of lung nodules to 2D image patch, we propose a weighted average image patch (WAIP) generation by averaging multiple slice images of lung nodule candidates. Moreover, to emphasize central slices of lung nodules, slice images are locally weighted according to Gaussian distribution and averaged to generate the 2D WAIP. With these extracted patches, 2D CNN is trained to achieve the classification of WAIPs of lung nodule candidates into positive and negative labels. We used LUNA 2016 public challenge database to validate the performance of our approach for false positive reduction in lung CT nodule classification. Experiments show our approach improves the classification accuracy of lung nodules compared to the baseline 2D CNN with patches from single slice image.

Conference Presentation

Citation Download Citation

Haeil Lee, Hansang Lee, Minseok Park, and Junmo Kim "Contextual convolutional neural networks for lung nodule classification using Gaussian-weighted average image patches", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013423 (3 March 2017); https://doi.org/10.1117/12.2253978

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