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. 2021 Feb 1;11(1):115-122.
doi: 10.31661/jbpe.v0i0.2001-1062. eCollection 2021 Feb.

Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries

M Toma  1   2 , Y Lu  3 , H Zhou  3 , J D Garcia  4
Affiliations

Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries

M Toma et al. J Biomed Phys Eng. .

Abstract

Computer simulations provide virtual hands-on experience when actual hands-on experience is not possible. To use these simulations in medical science, they need to be able to predict the behavior of actual processes with actual patient-specific geometries. Many uncertainties enter in the process of developing these simulations, starting with creating the geometry. The actual patient-specific geometry is often complex and hard to process. Usually, simplifications to the geometry are introduced in exchange for faster results. However, when simplified, these simulations can no longer be considered patient-specific as they do not represent the actual patient they come from. The ultimate goal is to keep the geometries truly patient-specific without any simplification. However, even without simplifications, the patient-specific geometries are based on medical imaging modalities and consequent use of numerical algorithms to create and process the 3D surface. Multiple users are asked to process medical images of a complex geometry. Their resulting geometries are used to assess how the user's choices determine the resulting dimensions of the 3D model. It is shown that the resulting geometry heavily depends on user's choices.

Keywords: Image Processing, Computer-Assisted, Errors; Patient-Specific Modeling; Thresholding; Uncertainty.

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Conflict of interest statement

Conflict of Interest: None

Figures

Figure 1
Figure 1
Results from the U.S. Food and Drug Administration’s (FDA) “Critical Path” project to validate computational fluid dynamics (CFD) methods [5]. The best and worst fitting results are shown from each (self-ascribed) category.
Figure 2
Figure 2
(a) Original (yellow) surface from the threshold segmentation compared to the final surface mesh (green) after smoothing techniques applied. Zoom-in to an attachment points between the chords and leaflets is shown to demonstrate the complexity of the geometry. (b) When the users returned their processed geometry the diameters of 6 chords (numbered) have been measured for comparison to analyze how user’s choices determine the outcome.
Figure 3
Figure 3
Number of diameter measurements, falling within a given range for each of the six chords.
See this image and copyright information in PMC

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