Abstract
Expanding on our previously developed method for inserting synthetic objects into clinical computed tomography (CT) data, we model a set of eight clinical tumors that span a range of geometries and locations within the lung. The goal is to create realistic but synthetic tumor data, with known volumes. The set of data we created can be used as ground truth data to compare volumetric methods, particularly for lung tumors attached to vascular material in the lung or attached to lung walls, where ambiguities for volume measurement occur. In the process of creating these data sets, we select a sample of often seen lung tumor shapes and locations in the lung, and show that for this sample a large fraction of the voxels representing tumors in the gridded data are partially filled voxels. This points out the need for volumetric methods that handle partial volumes accurately.
This contribution of NIST, an agency of the U.S. government, is not subject to copyright.
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References
Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., Henschke, C.I.: Three-Dimensional Segmentation and Growth-Rate Estimation of Small Pulmonary Nodules in Helical CT Images. IEEE Trans. on Medical Imaging 22(10) (October 2003)
Reeves, A.P., Chan, A.B., Yankelevitz, D.F., Henschke, C.I., Kressler, B., Kostis, W.J.: On measuring the change in size of pulmonary nodules. IEEE Trans. Med. Imaging 25(4), 435–450 (2006)
Mendonca, P., Bhotika, R., Sirohey, S., Turner, W., Miller, J., Avila, R.S.: Model-based Analysis of Local Shape for Lesion Detection in CT Lung Images. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 688–695. Springer, Heidelberg (2005)
McCulloch, C.C., Kaucic, R.A., Mendonca, P.R., Walter, D.J., Avila, R.S.: Model-based Detection of Lung Nodules in Computed Tomography Exams. Academic Radiology (March 2004)
Preim, B., Bartz, D.: Image Analysis for Medical Visualization. Visualization in Medicine, 83–131 (2007)
Preim, B., Bartz, D.: Exploration of Dynamic Medical Volume Data. Visualization in Medicine, 83–131 (2007)
Das, M., Ley-Zaporozhan, J., Gietema, H.A., Czech, A., Nuhlenbruch, G., Mahnken, A.H., Katoh, M., Bakai, A., Salganicoff, M., Diederich, S., Prokop, M., Kauczor, H., Gunther, R.W., Wildberger, J.E.: Accuracy of automated volumetry of pulmonary nodules across different mutlislice CT scanners. Eur. Radiol. 17, 1979–1984 (2007)
Ko, J.P., Rusinek, H., Jacobs, E.L., Babb, J.S., Betke, M., McGuinness, G., Naidich, D.P.: Small Pulmonary Nodules: Volume Measurent at Chest CT-Phantom Study. Radiology 228, 864–870 (2003)
Peskin, A.P., Kafadar, K., Dima, A., Bernal, J., Gilsinn, D.: Synthetic Lung Tumor Data Sets for Comparison of Volumetric Algorithms. In: The 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, pp. 43–47 (July 2009)
NCI Reference Image Database to Evaluate Response (RIDER) database, https://wiki.nci.nih.gov/display/CIP/RIDER
Levine, Z.H., Borchardt, B.R., Brandenburg, N.J., Clark, C.W., Muralikrishnan, B., Shakarji, C.M., Chen, J.J., Siege, E.L.: RECIST versus volume measurement in medical CT using ellipsoids of known size. Optics Express 18(8), 8151–8159 (2010)
Peskin, A.P., Kafadar, K., Santos,A.M., Haemer,G.G.: Robust Volume Calculations of Tumors of Various Sizes. In: The 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition (July 2009)
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Peskin, A.P., Dima, A.A. (2010). Modeling Clinical Tumors to Create Reference Data for Tumor Volume Measurement. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_72
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DOI: https://doi.org/10.1007/978-3-642-17274-8_72
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