Micro CT based truth estimation of nodule volume

L. M. Kinnard; M. A. Gavrielides; K. J. Myers; R. Zeng; B. Whiting; S. Lin-Gibson; N. Petrick

doi:10.1117/12.844381

9 March 2010 Micro CT based truth estimation of nodule volume

L. M. Kinnard, M. A. Gavrielides, K. J. Myers, R. Zeng, B. Whiting, S. Lin-Gibson, N. Petrick

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 762414 (2010) https://doi.org/10.1117/12.844381
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

With the advent of high-resolution CT, three-dimensional (3D) methods for nodule volumetry have been introduced, with the hope that such methods will be more accurate and consistent than currently used planar measures of size. However, the error associated with volume estimation methods still needs to be quantified. Volume estimation error is multi-faceted in the sense that there is variability associated with the patient, the software tool and the CT system. A primary goal of our current research efforts is to quantify the various sources of measurement error and, when possible, minimize their effects. In order to assess the bias of an estimate, the actual value, or "truth," must be known. In this work we investigate the reliability of micro CT to determine the "true" volume of synthetic nodules. The advantage of micro CT over other truthing methods is that it can provide both absolute volume and shape information in a single measurement. In the current study we compare micro CT volume truth to weight-density truth for spherical, elliptical, spiculated and lobulated nodules with diameters from 5 to 40 mm, and densities of -630 and +100 HU. The percent differences between micro CT and weight-density volume for -630 HU nodules range from [-21.7%, -0.6%] (mean= -11.9%) and the differences for +100 HU nodules range from [-0.9%, 3.0%] (mean=1.7%).

Citation Download Citation

L. M. Kinnard, M. A. Gavrielides, K. J. Myers, R. Zeng, B. Whiting, S. Lin-Gibson, and N. Petrick "Micro CT based truth estimation of nodule volume", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762414 (9 March 2010); https://doi.org/10.1117/12.844381

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KEYWORDS

Computed tomography

Image segmentation

3D image processing

Spherical lenses

Lung

CT reconstruction

Error analysis

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