na-mic national alliance for medical image computing segmentation core 1-3 meeting, may. 22-23,...
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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?