# Projects:MultiscaleShapeSegmentation

Back to NA-MIC Collaborations, Stony Brook University Algorithms, UNC Algorithms, GE Engineering, Kitware Engineering, Harvard DBP 1

# Multiscale Shape Segmentation

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus.

# Description

## Shape Representation and Prior

The overview of our shape representation is given in Figure 1. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population (Figure 2). We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations (Figure 4) [1].

## Segmentation

Based on this representation, we derive a parametric active surfaceIn evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner.

## Results

We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia [2]. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

# Key Investigators

- Georgia Tech Algorithms: Delphine Nain, Aaron Bobick, Allen Tannenbaum
- UNC Algorithms: Martin Styner
- GE Engineering: Jim Miller
- Kitware Engineering: Luis Ibanez
- Harvard DBP 1: Steven Haker, James Levitt, Marc Niethammer, Sylvain Bouix, Martha Shenton

# Publications

*In Print*