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A Cardiac Shape Model for Segmentation Uncertainty Quantification.

Jess D Tate¹, Shireen Elhabian¹, Nejib Zemzemi²

¹University of Utah, Salt Lake City, UT, USA.

Computing in Cardiology

|April 28, 2022

Summary

This summary is machine-generated.

Creating a unified cardiac shape model improves analysis of segmentation variability. The hybrid multidomain approach in ShapeWorks yielded the most accurate ventricular model, aiding computational pipelines.

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Area of Science:

Computational modeling
Medical image analysis
Cardiovascular research

Background:

Cardiac image segmentation variability impacts patient-specific computational models.
Developing statistical shape models of ventricles is challenging.
Previous work focused on separate epicardial and endocardial models.

Purpose of the Study:

To create a unified statistical shape model of the ventricles including both epicardium and endocardium.
To evaluate different ShapeWorks techniques for generating this unified model.
To assess the impact of segmentation variability on model characteristics.

Main Methods:

Developed a unified shape model incorporating epicardium and endocardium.
Tested four ShapeWorks techniques: standard, multidomain, hybrid multidomain, and geodesic distance.
Compared models generated using all segmentations versus a subset.

Main Results:

All tested methods captured spatially dependent segmentation variability (wall thickness, diameter, basal truncation).
Multidomain and hybrid multidomain methods utilized all eleven segmentations.
Geodesic distance method used a subset of four segmentations.
The hybrid multidomain approach produced the most accurate model with the fewest points.

Conclusions:

The hybrid multidomain approach offers a robust method for creating accurate cardiac ventricular shape models.
This unified model can better represent segmentation variability in computational pipelines.
The findings suggest the hybrid multidomain method is suitable for most applications requiring precise cardiac modeling.