Prediction of Organ Geometry from Demographic and Anthropometric Data based on Supervised Learning Approach using Statistical Shape Atlas

Yoshito Otake, Carneal Catherine, Blake Lucas, Gaurav Thawait, John Carrino, Brian Corner, Marina Carboni, Barry DeCristofano, Michale Maffeo, Andrew Merkle, Mehran Armand

Abstract

We propose a method relating internal human organ geometries and non-invasively acquired information such as demographic and anthropometric data. We first apply a dimensionality reduction technique to a training dataset to represent the organ geometry with low dimensional feature coordinates. Regression analysis is then used to determine a regression function between feature coordinates and the external measurements of the subjects. Feature coordinates for the organ of an unknown subject are then predicted from external measurements using the regression function, subsequently the organ geometry is estimated from the feature coordinates. As an example case, lung shapes represented as a point distribution model was analyzed based on demographic (age, gender, race), and several anthropometric measurements (height, weight, and chest dimensions). The training dataset consisted of 124 topologically consistent lung shapes created from thoracic CT scans. The prediction error of lung shape of an unknown subject based on 11 demographic and anthropometric information was 10.71 ± 5.48 mm. This proposed approach is applicable to scenarios where the prediction of internal geometries from external parameters is of interest. Examples include the use of external measurements as a prior information for image quality improvement in low dose CT, and optimization of CT scanning protocol.

References

  1. Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling,: Theory and applications (2nd ed.). New York: Springer.
  2. Gordon, C. C., Churchill, T., Clauser, C. E., Bradtmiller, B., McConville, J. T., Tebbetts, I., Walker, R. A. (1989). 1988 Anthropometric Survey of US Army Personnel: Methods and Summary Statistic. Technical Report NATICK/TR-89/044, United States Army Natick Research, Development and Engineering Center, Natick, MA, USA.
  3. Chintalapani, G., Murphy, R., Armiger, R. S., Lepisto, J., Otake, Y., Sugano, N., et al. (2010). Statistical atlas based extrapolation of CT data. Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 7625(1), 762539.
  4. Ehrhardt, J., Werner, R., Schmidt-Richberg, A., & Handels, H., (2011). Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. Medical Imaging, IEEE Transactions on, 30(2), 251-265.
  5. Ellingsen, L. M., Chintalapani, G., Taylor, R. H., & Prince, J. L., (2010). Robust deformable image registration using prior shape information for atlas to patient registration. Computerized Medical Imaging and Graphics, 34(1), 79-90.
  6. Frangi, A. F., Rueckert, D., Schnabel, J. A., & Niessen, W. J., (2002). Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling. Medical Imaging, IEEE Transactions on, 21(9), 1151-1166.
  7. Hongkai Wang, Stout, D. B., & Chatziioannou, A. F., (2012). Estimation of mouse organ locations through registration of a statistical mouse atlas with micro-CT images. Medical Imaging, IEEE Transactions on, 31(1), 88-102.
  8. Press, W. H., (2007). Numerical recipes : The art of scientific computing (3rd ed.). Cambridge, UK ;New York: Cambridge University Press.
  9. S, B., Robinette, K. M., & Daanen, H. A. M., (2002). Civilian American and European Surface Anthropometry Resource (CAESAR), Final Report, Volume II: Descriptions. AFRL-HE-WP-TR-2002- 0170. Wright-Patterson AFB OH, USA.
  10. Shepard, D., (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 23rd ACM National Conference, pp. 517-524.
  11. Tenenbaum, J. B., Silva, V. d., & Langford, J. C., (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319- 2323.
Download


Paper Citation


in Harvard Style

Otake Y., Catherine C., Lucas B., Thawait G., Carrino J., Corner B., Carboni M., DeCristofano B., Maffeo M., Merkle A. and Armand M. (2013). Prediction of Organ Geometry from Demographic and Anthropometric Data based on Supervised Learning Approach using Statistical Shape Atlas . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 365-374. DOI: 10.5220/0004263803650374


in Bibtex Style

@conference{icpram13,
author={Yoshito Otake and Carneal Catherine and Blake Lucas and Gaurav Thawait and John Carrino and Brian Corner and Marina Carboni and Barry DeCristofano and Michale Maffeo and Andrew Merkle and Mehran Armand},
title={Prediction of Organ Geometry from Demographic and Anthropometric Data based on Supervised Learning Approach using Statistical Shape Atlas},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={365-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004263803650374},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Prediction of Organ Geometry from Demographic and Anthropometric Data based on Supervised Learning Approach using Statistical Shape Atlas
SN - 978-989-8565-41-9
AU - Otake Y.
AU - Catherine C.
AU - Lucas B.
AU - Thawait G.
AU - Carrino J.
AU - Corner B.
AU - Carboni M.
AU - DeCristofano B.
AU - Maffeo M.
AU - Merkle A.
AU - Armand M.
PY - 2013
SP - 365
EP - 374
DO - 10.5220/0004263803650374