NA-MICNational Alliance for Medical Image Computing http://na-mic.org

Segmentation

Core 1-3 Meeting, May. 22-23, 2008 - SLC, UT

Georgia Tech/JHU

Prostate Segmentation & Registration Framework

Yi Gao (Georgia Tech), Allen Tannenbaum (Georgia Tech), Gabor Fichtinger (JHU)

Background

• Under the roadmap project: Brachytherapy Needle Positioning Robot Integration.

• Auto/Semiauto segmentation.

• Registration:– Between modalities: US/MRI– Before/during therapy

Segmentation

• Two approaches:– Random Walks(RW)

• RW + post process• Toward automatic segmentation

– Spherical wavelet shape based method

Random walks

• Random Walks(RW)– Result → – Less interaction– C++ code

Shape based method

• Spherical wavelet shape based method– Shape learning– ITK Spherical wavelet transformation– Shape based segmentation

Shape based method

• Spherical wavelet shape based method– Shape learning– ITK Spherical wavelet transformation– Shape based segmentation

Shape learning

• Align segmented shapes.– Registration under Similarity transform.

• Learn aligned shapes.– Statistical learning: PCA, KPCA, GPCA

Shape learning

• Align segmented shapes.– Registration under Similarity transform.

• Learn aligned shapes.– Statistical learning: PCA, KPCA, GPCA

Registration

• Common region extraction– Used as landmark

• Rigid/Deformable registration– Particle filter/Kalman filter– Optimal mass transportation

Landmark extraction

• Chan-Vese on manifold– Extract featured region on surface.– Feature defined by a function.

Color depicts a scalar function defined on a surface.

Landmark extraction, cont.

Landmark based Registration

• Concave belly of prostate– Common among all prostate

• Used as soft-landmark in registration.

UNC/MIND

Lesion Segmentation

Marcel Prastawa (Utah), Guido Gerig (Utah), Jeremy Bockholt (MIND)

Lesion Segmentation

T1

T2

before after

MIND Lupus Lesion

Iowa/MIND

Bayesian Classification of Lupus Lesions

Vincent A. Magnotta (Iowa), Jeremy Bockholt (MIND), Peter Pellegrino (Iowa)

Algorithm Overview

• Tissue classification algorithm coupled with lesion identification

• Required Inputs– T1, T2, and FLAIR images that have

been spatially normalized and bias field corrected

– Definition of the brain– Currently uses BRAINS Autoworkup

pipeline to fulfill these requirements

Algorithm• Uses K-means classification

– Initial estimate of GM, WM, and CSF based on minimum, mean, and standard deviation from T1 weighted image

– Kmeans segmentation into GM, WM, and CSF from T1 weighted image

• Lesion from FLAIR Images– Threshold FLAIR image based on mean and

standard deviation within the brain– Eliminate lesion voxels adjacent to CSF– Remaining lesion voxels from the Kmeans

classification are used to relabel the Kmeans labelmap with a Lesion value

Bayesian Classification• Define exemplars for classes

– Randomly sample 1000 points from GM, WM, CSF, and Lesion labels

– Used to define the means and variance for the classes

• Define class priors– Extract each class from labelmap generated in

previous step and filter with a 2mm gaussian filter

• Run multi-modal Bayesian classifier– T1, T2, and FLAIR images input

Results

MIT/Harvard

Tissue Classification

Kilian Pohl (MIT/BWH), Brad Davis (Kitware), Sylvain Bouix (Harvard), Marek Kubicki (Harvard), Martha Shenton (Harvard), Sandy Wells (BWH), Polina Golland (MIT)

Slicer 3 Module

EM-Segmenter

• Intensity normalization

• Structure hierarchy

• Registration– Atlas-to-subject

– Multimodal

• Applications: – Tissue classification– Structure parcelation– MS lesions segmentation

Georgia Tech/Harvard

Label Space Segmentation

Jimi Malcolm (Georgia Tech), Allen Tannenbaum (Georgia Tech), Yogesh Rathi (Harvard)

Problem: Constructing an anatomical model for multiple, covarying regions

- Slice from labeled brain:

State of the art - Signed distance maps: develop artifacts along interface

between regions, small variations on interface cause large perturbations far away

- Binary vectors: background bias during registration - LogOdds: natural probabilistic interpretation, but uses the

above intermediate representations thus incurring similar problems

Label Space

Label Space

Label Space:

- regular simplex:

- natural algebraic manipulation

- direct probabilistic interpretation

- unbiased toward any label

Label Space

Experiments:

- smoothing, interpolation

- registration

- probabilistic atlases

Questions?