Document Type

Article

Publication Date

1-1-2018

Publication Source

Journal of Medical Imaging

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.

ISBN/ISSN

2329-4302

Document Version

Published Version

Comments

Document is made available in compliance with the publisher's policy on self-archiving. Permission documentation is on file. To view the paper on the publisher's website, use the DOI: https://doi.org/10.1117/1.JMI.5.1.014504

Publisher

Society of Photo-optical Instrumentation Engineers

Volume

5

Peer Reviewed

yes

Issue

1

Keywords

computed tomography, computer-aided detection, downsampling, lung nodules, slice thickness, University of Dayton Electro-optics and Photonics

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