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Towards Estimating the Uncertainty Associated with Three-Dimensional Geometry Reconstructed from Medical Image Data.

Marc Horner¹, Stephen M Luke², Kerim O Genc³

¹ANSYS, Inc., Evanston, IL.

Journal of Verification, Validation, and Uncertainty Quantification

|August 29, 2020

Summary

This summary is machine-generated.

Image artifacts and segmentation variability can impact patient-specific treatment plans. This study quantifies measurement errors in medical imaging, finding up to 4% error in physical scans, highlighting the need for uncertainty estimation in digital image analysis.

Keywords:

image reconstruction medical imaging patient-specific anatomy systematic error

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Area of Science:

Medical Imaging and Image Analysis
Computational Modeling
Biomedical Engineering

Background:

Patient-specific computational modeling relies heavily on accurate medical imaging for treatment planning.
Image artifacts and segmentation processes introduce variability and potential inaccuracies in anatomical data extraction.
Quantifying uncertainty in medical image analysis is crucial for reliable patient-specific treatment plans.

Purpose of the Study:

To develop and analyze image datasets to estimate the uncertainty associated with extracting anatomical data from medical images.
To assess the impact of image artifacts and segmentation variability on the accuracy of anatomical measurements.
To investigate methods for improving segmentation accuracy in medical image analysis.

Main Methods:

Development of two image datasets: one from a "virtual voxelization" of a CAD model (idealized) and another from CT scanning of physical spherical phantoms.
Standard image analysis procedures were applied to extract anatomical data.
Investigation of established thresholding procedures to enhance segmentation accuracy.

Main Results:

For the idealized sphere, diameter error was ≤2% with 5+ voxels across the diameter.
Measurement error increased to approximately 4% for physical phantoms with similar voxelization.
Thresholding procedures were adapted to explore improvements in segmentation accuracy.

Conclusions:

Image acquisition artifacts and segmentation variability introduce measurable errors in anatomical data extraction.
The study quantifies the impact of these errors, showing higher uncertainty in physical scans compared to idealized models.
Accurate estimation of uncertainty is essential for reliable patient-specific computational modeling and treatment planning.