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Publication Citation: Synthetic Lung Tumor Data Sets for Comparison of Volumetric Algorithms

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Author(s): Adele P. Peskin; Alden A. Dima; Javier Bernal; David E. Gilsinn; Karen Kafadar;
Title: Synthetic Lung Tumor Data Sets for Comparison of Volumetric Algorithms
Published: July 13, 2009
Abstract: The change in pulmonary nodules over time is an important indicator of malignant tumors. It is therefore important to be able to measure change in the size of tumors from computed tomography (CT) data taken at different times and on potentially different CT machines. A particular tumor may or may not be divided into slices at exactly the same places on two different sets of scans. The pixel distributions and average background values may also not be the same between two different sets of data. Standardized sets of data are needed to compare techniques for calculating tumor volumes and/or the change in tumor size between two sets of data. Combining phantom data with realistic lung data could provide realistic standardized data sets, which include many of the measurement challenges that are not available in pure phantom data alone. We present a set of synthetic lung tumor data in which synthetic tumors of known volume are embedded in real lung CT data in different background settings in the lung.
Proceedings: The 2009 World Congress in Computer Science Computer Engineering and Applied Computing
Location: Las Vegas, NV
Dates: July 13-16, 2009
Keywords: image processing;segmentation;synthetic data;reference data
Research Areas: Diagnostics, Imaging, Medical Devices
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