CONFERENCE PROCEEDINGS
Advanced Search
Home > Journals > J. Med. Imag. > Volume 5 > Issue 1 > Article
Open Access
19 February 2018 Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses
Barath Narayanan Narayanan, Russell Craig Hardie, Temesguen Messay Kebede
Author Affiliations +
Journal of Medical Imaging, Vol. 5, Issue 1, 014504 (February 2018). https://doi.org/10.1117/1.JMI.5.1.014504
Abstract
We study the performance of a computer-aided detection (CAD) system for lung nodules in computed tomography (CT) as a function of slice thickness. In addition, we propose and compare three different training methodologies for utilizing nonhomogeneous thickness training data (i.e., composed of cases with different slice thicknesses). These methods are (1) aggregate training using the entire suite of data at their native thickness, (2) homogeneous subset training that uses only the subset of training data that matches each testing case, and (3) resampling all training and testing cases to a common thickness. We believe this study has important implications for how CT is acquired, processed, and stored. We make use of 192 CT cases acquired at a thickness of 1.25 mm and 283 cases at 2.5 mm. These data are from the publicly available Lung Nodule Analysis 2016 dataset. In our study, CAD performance at 2.5 mm is comparable with that at 1.25 mm and is much better than at higher thicknesses. Also, resampling all training and testing cases to 2.5 mm provides the best performance among the three training methods compared in terms of accuracy, memory consumption, and computational time.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Citation Download Citation
Barath Narayanan Narayanan, Russell Craig Hardie, and Temesguen Messay Kebede "Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses," Journal of Medical Imaging 5(1), 014504 (19 February 2018). https://doi.org/10.1117/1.JMI.5.1.014504
Received: 16 September 2017; Accepted: 25 January 2018; Published: 19 February 2018
JOURNAL ARTICLE
 10 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Lens.org Logo
CITATIONS
Cited by 31 scholarly publications.
Advertisement
Advertisement
KEYWORDS
Lung
Computed tomography
Computer aided diagnosis and therapy
CAD systems
Computer aided design
Computing systems
Computer simulations
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top