Prediction of organ geometry from demographic and anthropometric data based on supervised learning approach using statistical shape atlas

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods
Pages365-374
Number of pages10
StatePublished - May 27 2013
Event2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013 - Barcelona, Spain
Duration: Feb 15 2013Feb 18 2013

Publication series

NameICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

Other

Other2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013
CountrySpain
CityBarcelona
Period2/15/132/18/13

Keywords

  • Allometry
  • Demographic and anthropometric data
  • Principal component analysis
  • Regression analysis
  • Statistical shape atlas
  • Supervised learning

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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