Towards Estimating the Uncertainty Associated with 3D Geometry Reconstructed from Medical Image Data

Published

November 1, 2019

Author(s)

Marc Horner, Karim O. Genc, Stephen M. Luke, Todd M. Pietila, Ross T. Cotton, Benjamin Ache, Kevin C. Townsend, Zachary H. Levine

Abstract

Patient-specific computational modeling is increasingly being used to assist with the visualization, planning and execution of medical treatments. This trend is placing more reliance on medical imaging to provide an accurate representation of anatomical structures. Digital image analysis is used to segment (extract) patient anatomical data before it is used in clinical assessment/planning. However, the presence of image artifacts, whether due to the physical object or the scanning process, can degrade image accuracy. The process of extracting anatomical structures from medical images introduces additional sources of variability, e.g., when thresholding or eroding a biological interface. An estimate of the uncertainty associated with extracting anatomical data would therefore assist the segmentation process.

Citation

Journal of Verification, Validation, and Uncertainty Quantification

Volume

18

Pub Type

Journals

Download Paper

DOI Link

Keywords

medical imaging , image reconstruction , patient-specific anatomy , systematic error

Citation

Horner, M. , Genc, K. , Luke, S. , Pietila, T. , Cotton, R. , Ache, B. , Townsend, K. and Levine, Z. (2019), Towards Estimating the Uncertainty Associated with 3D Geometry Reconstructed from Medical Image Data, Journal of Verification, Validation, and Uncertainty Quantification, [online], https://doi.org/10.1115/1.4045487 (Accessed October 8, 2025)