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Updated: Jun 10, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

OPTIMIZATION-DRIVEN STATISTICAL MODELS OF ANATOMIES USING RADIAL BASIS FUNCTION SHAPE REPRESENTATION.

Hong Xu¹, Shireen Y Elhabian¹

¹Scientific Computing and Imaging Institute, Kahlert School of Computing, University of Utah, Salt Lake City, UT, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 11, 2024

Summary

This summary is machine-generated.

This study introduces a novel particle-based shape modeling (PSM) method using traditional optimization for precise anatomical shape analysis. The approach offers greater control and more informative statistical models compared to deep learning alternatives.

Keywords:

Optimization Polyharmonic Splines Radial Basis Function Interpolation Statistical Shape Modeling

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Area of Science:

Medical imaging
Computational anatomy
Biomedical engineering

Background:

Particle-based shape modeling (PSM) quantifies anatomical shape variability using optimization to place particles on 3D surfaces.
Recent deep learning methods use implicit radial basis functions for complex geometries, but lack control.

Purpose of the Study:

To adapt PSM using traditional optimization for enhanced control and informative statistical models.
To provide a non-black-box alternative to deep learning approaches in shape analysis.

Main Methods:

Developed a novel PSM approach combining eigenshape and correspondence losses.
Employed traditional optimization for precise control over model characteristics.
Allowed particles greater freedom to navigate surfaces for richer statistical insights.

Main Results:

Demonstrated superior efficacy compared to state-of-the-art methods on two real datasets.
Empirically validated the chosen loss functions.
Generated more informative statistical shape models.

Conclusions:

The proposed traditional optimization PSM offers precise control and improved statistical models.
This method provides a valuable, transparent alternative to deep learning in anatomical shape analysis.