slicer3 emsegment tutorial

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

Slicer3 EMSegment Tutorial

January 2008 NAMIC All-Hand’s Meeting

Brad Davis, Yuman Yuan, Sebastien Barre, Will Schroeder, Polina Goland, Ron Kikinis, Kilian Pohl

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

Program

• EM Segmenter basics• Algorithm “views” in Slicer3• Slicer3 EM segment under the hood• Tutorial

– Data– Simple Segmentation Interface– Template Builder Interface

• Questions (all week…)


EM Segment BasicsT1-weighted MRI T2-weighted MRI

Automatic Segmentation

Cortical Spinal Fluid

White Matter SuperiorTemporal Gyrus

MiddleTemporal Gyrus

Figure: Pohl et al.


EM Segment Basics

Figures: Pohl et al.


Atlas Images

Target Images

Parameters

Segmentation Result

EM Segment Basics

GM

WM

CSF

Air

Skin/Skull


Atlas Images

Parameters

Segmentation Result

EM Segment Basics

• Collection of images (input channels) that will be segmented

T1 T2

Target Images


Atlas Images

Target Images

Parameters

Segmentation Result

EM Segment Basics

• Hierarchy of anatomical structures– Intensity distributions, prior probabilities

• Default preprocessing– Intensity normalization, registration

• EM segmentation algorithm– Smoothing, weights, # iterations

• Stored as XML MRML file

Root

ICC Background

Air Skin/Skull

GM WM CSF


Target Images

Parameters

Segmentation Result

EM Segment Basics

• Atlas images (spatial priors) – Aligned in common coordinate frame

GM Air Skin/SkullWMCSF

Atlas Images


Atlas Images

Target Images

Parameters

Segmentation Result

EM Segment Basics

• Labelmap volume – Voxel values

specified in parameters

Root

ICC Background

Air Skin/Skull

GM WM CSF


Algorithm Development

• 12-year history of algorithm development– Wells et al. 1996 (TMI): EM framework for simultaneous

estimation of bias field and label map– Kapur et al. 1999 (PhD Thesis): Model noise via Markov

Random Field – Van Leemput et al. 1999 (TMI): Non-spatial tissue

priors– Pohl et al.

• 2002 (MICCAI): Deformable registration to align atlas• 2004 (ISBI): Hierarchical framework to model anatomical

dependencies• 2007 (TMI): Up-to-date, detailed description


Slicer3

Segmentation Code

Preprocessing

Wizard GUI

Recent Development Effort• Goals:

– Easy to use– Adapts to a variety of

scenarios– Is a research tool– Simplified interfaces– Minimize

preprocessing requirements

– Validation and testing

ITK/VTK


EMSegmenter Logic / Preprocessing / Algorithm

Slicer3 GUIEM Template

BuilderModule

EM SimpleModule

CommandLine

EM CommandLine

Executable

Slicer3 EM segment “views”

• Advanced interface for creating/editing segmentation parameters

• Simple interface for applying parameters to new images

• Command-line interface for script/batch processing


Template Builder View

• Runs inside Slicer3

• Purpose: Build/Adapt parameter sets

• Run segmentation• Wizard guides

user through interface panels


`One-Click’ View

• Runs inside Slicer3

• Purpose: Simple interface for segmenting new data

• Requires predefined parameter set


Command-line View

• EMSegmentCommandLine executable bundled with Slicer3

• Purpose: Scripted/batch processing• Requires predefined parameter set• Specify new target/atlas images on

command line

Atlas Images

Target Images

Parameters

Segmentation Result


SpecifyInputs Parameters Target Images Atlas Images

Slicer3 EM Segment Workflow


SpecifyInputs

DefaultPre-

Processing

Parameters Target Images Atlas Images

Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration



SpecifyInputs

DefaultPre-

Processing


Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration

Segmentation EM Segment Algorithm: Pohl et al.



SpecifyInputs

DefaultPre-

Processing


Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


ReviewResults

Slicer3 Slice

Views

Slicer3 Model

Maker

External

Program



DefaultPre-

Processing

Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration

New Development: Default Preprocessing

• Lower bar for entry• Starting point for building effective pipeline• Not intended for all scenarios• No interface for complex parameter

tweaking• Easily bypassed by advanced users


DefaultPre-

Processing

Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


• VTK filter by Kilian Pohl

• Scale intensities to achieve predefined mean– Heuristics determine histogram cutoffs

• Default settings for T1, T2 images


DefaultPre-

Processing

Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


• Target image 1 is fixed image• Perform registration for each additional

target image– Align image centers– Rigid registration, mutual information, gradient

descent optimization (ITK)– Coarse-to-fine


DefaultPre-

Processing

Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


• Target image 1 is fixed image• Perform registration with selected atlas image

– Align image centers, Rigid registration, B-Spline registration– Mutual information, (normalized cross-correlation, sse, not yet tested)– Rigid: gradient descent, B-Spline: ITK LBFGSB optimizer, 5x5x5 knot

points – Coarse-to-fine– Fast/Standard/Slow: sample points, max iterations– Resample all atlas images via transform; guess background value


Default Registration Results


Tutorial Data Walkthrough 1/3

• README: extra release notes• Predefined parameter file

– MR T1 & T2 brain segmentation: White matter, gray matter, CSF

• Target Images– ImageData_Input sub directory– Target: T1 & T2 images used in parameter file– Target_NewPatient1, Target_NewPatient2:

New data to try with the parameter file



• Atlas Images– ImageData_Input sub directory– Atlas: atlas images used in parameter file– Atlas_NewAtlas: Extra atlas data for

demonstration purposes (currently a copy of first atlas)



• Low resolution data for testing – Full sized data: 30

minutes– Low resolution

data: 1 minute

• Data, parameters have “_small” appended to filename


`One-Click’ Module Tutorial 1/3

• EMSegment tutorial data is required

• Within Slicer3, open EMSegment Simple Module



• MRML Scene: Select the tutorial parameter set

• Result Labelmap: Select a new image filename

• Target Volumes: Select input target volumes from one of the target directories (e.g., Target_NewPatient1)

• Note: target volume order is important

• Click `Apply’, wait (~30 minutes for full size images)…



• Review the results in Slicer3– Load target images– Load result labelmap– Use slice views to review

segmentation results

• Apply Model Maker to generate surfaces


EMSegment Command-line Examples

• Script examples available in tutorial directory – New images– New atlas– New image and atlas– See README


Template Builder Tutorial

• EMSegment tutorial data is required

• Within Slicer3, open EMSegment Module



• Load tutorial parameters– File->Load Scene– Select ‘mrml’ file included

with tutorial

• Explore volumes included with scene– Target volumes, atlas

volumes– An old segmentation result

is also included



• Step 1: Choose EMSegmenter parameter set

• For now, use tutorial set• Later use this interface

to create a new parameter set



• Define anatomical structures

• Right click to add or delete nodes

• Label corresponds to eventual voxel values in segmentation result



• Choose an atlas volume for each leaf node

• To change or add new atlas volumes– Load new volumes into

Slicer3– Select new volumes at

this step



• Choose target images• To change or add target

images– Load new volumes into

Slicer3

– Select new volumes at this step

• You can reorder target images; order is important



• You can choose to align target images

• First target is fixed image

• Rigid, mutual information registration



• You can choose to normalize target images

• Simple, default strategy• Default parameter sets

available from pulldown



• Intensity distributions define appearance of each leaf structure

• Gaussian• Dimensionality equal to

number of target images• Two methods

– Sample voxels from images– Specify mean and covariance

manually• Tip: sample first, then fine

tune manually



• Sample voxels from images– Load first target image

into Slicer3 slicer view– Choose anatomical

structure– Choose `manual

sampling’– Ctrl-left-click on image

to add voxels



• Sample voxels from images (continued)– To remove an unwanted

sample right-click on it and choose “remove”

– Change back to “manual” mode to tweak distribution



• Segmentation parameters for every tree node

• Influence– Prior weight relative to

other structures– Atlas– Input channels– Smoothing (parent nodes

only)

• Stopping Conditions



• Atlas-to-target registration parameters

• Moving image registered to first target image– You can choose any image

loaded into Slicer3

• Same transformation applied to all atlas images before segmentation begins



• Select output and run registration

• You can troubleshoot preprocessing by saving intermediate results

• Choose an output labelmap• ROI governs segmentation

processing (one-based, not zero-based)


Acknowledgements

• Wendy Plesniak, SPL BWH

• Steve Pieper, Isomics

• Luis Ibáñez, Kitware

• Sylvain Bouix, PNL BWH

• William Wells, BWH

• Funding provided by NAMIC

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

Questions…


SpecifyInputs

DefaultPre-

Processing


Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


ReviewResults

Slicer3 Slice

Views

Slicer3 Model

Maker

External

Program



Atlas Images

Target Images

Parameters

Segmentation Result

SpecifyInputs

DefaultPre-

Processing


Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


ReviewResults

Slicer3 Slice

Views

Slicer3 Model

Maker

External

Program

slicer3 emsegment tutorial

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