emsegtutorial-ahm2008

7/31/2019 EMSegTutorial-AHM2008

1/49

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

Slicer3 EMSegment

Tutorial

January 2008 NAMIC All-Hands Meeting

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


2/49

National Alliance for Medical Image Computinghttp://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)


3/49


EM Segment BasicsT1-weighted MRI T2-weighted MRI

Automatic Segmentation

CorticalSpinal Fluid

White MatterSuperiorTemporal Gyrus

MiddleTemporal Gyrus

Figure: Pohl et al.


4/49


EM Segment Basics

Figures: Pohl et al.


5/49


Atlas Images

Target Images

Parameters

Segmentation Result

EM Segment Basics

GM

WM

CSF

Air

Skin/Skull


6/49


Atlas Images

Parameters

Segmentation Result

EM Segment Basics

Collection of images (input channels) thatwill be segmented

T1 T2

Target Images


7/49


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


8/49


Target Images

Parameters

Segmentation Result

EM Segment Basics

Atlas images (spatial priors)

Aligned in common coordinate frame

GM Air Skin/SkullWMCSF

Atlas Images


9/49


Atlas Images

Target Images

Parameters

Segmentation Result

EM Segment Basics

Labelmapvolume

Voxel valuesspecified inparameters

Root

ICC Background

Air Skin/Skull

GM WM CSF


10/49


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 anatomicaldependencies

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


11/49


Slicer3

Segmentation

Code

Preprocessing

Wizard GUI

Recent Development Effort

Goals: Easy to use

Adapts to a variety ofscenarios

Is a research tool

Simplified interfaces

Minimizepreprocessing

requirements

Validation and testing

ITK/VTK


12/49


EMSegmenter Logic / Preprocessing / Algorithm

Slicer3 GUIEM Template

BuilderModule

EM Simple

Module

Command

Line

EM CommandLine

Executable

Slicer3 EM segmentviews

Advanced interface for creating/editingsegmentation parameters

Simple interface for applying parameters to newimages

Command-line interface for script/batchprocessing


13/49


Template Builder View

Runs insideSlicer3

Purpose:

Build/Adaptparameter sets

Run segmentation

Wizard guidesuser throughinterface panels


14/49


`One-Click View

Runs insideSlicer3

Purpose: Simple

interface forsegmenting newdata

Requirespredefinedparameter set


15/49


Command-line View

EMSegmentCommandLineexecutable bundled with Slicer3

Purpose: Scripted/batch processing

Requires predefined parameter set

Specify new target/atlas images oncommand line

Atlas Images

Target Images

Parameters

Segmentation Result


16/49


SpecifyInputs

Parameters Target Images Atlas Images

Slicer3 EM SegmentWorkflow


17/49


SpecifyInputs

DefaultPre-Processing


Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration



18/49


SpecifyInputs



Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration

Segmentation EM Segment Algorithm: Pohl et al.



19/49


SpecifyInputs



Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


ReviewResults

Slicer3 Slice

Views

Slicer3 Model

Maker

External

Program



20/49


DefaultPre-

Processing

Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration

New Development: DefaultPreprocessing

Lower bar for entry Starting point for building effective pipeline

Not intended for all scenarios

No interface for complex parametertweaking

Easily bypassed by advanced users


21/49


DefaultPre-

Processing

Target Image

Normalization

Target-to-target

Registration

Atlas-to-target

Registration


VTK filter by Kilian Pohl

Scale intensities to achieve predefinedmean

Heuristics determine histogram cutoffs

Default settings for T1, T2 images


22/49


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, gradientdescent optimization (ITK)

Coarse-to-fine


23/49


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


24/49


Default RegistrationResults


25/49


Tutorial Data Walkthrough1/3

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


26/49



Atlas Images

ImageData_Input sub directory

Atlas: atlas images used in parameter file

Atlas_NewAtlas: Extra atlas data fordemonstration purposes (currently a copy offirst atlas)


27/49



Low resolutiondata for testing

Full sized data: 30

minutes Low resolution

data: 1 minute

Data, parameters

have _smallappended tofilename


28/49


`One-Click Module Tutorial1/3

EMSegment tutorial data is required Within Slicer3, open EMSegment Simple

Module


29/49



MRML Scene: Select the tutorialparameter set

Result Labelmap: Select a newimage filename

Target Volumes: Select inputtarget volumes from one of the targetdirectories (e.g.,Target_NewPatient1)

Note: target volume order isimportant

Click `Apply, wait (~30 minutes forfull size images)


30/49



Review the results inSlicer3

Load target images

Load result labelmap Use slice views to review

segmentation results

Apply Model Maker to

generate surfaces


31/49


EMSegment Command-lineExamples

Script examples available in tutorialdirectory

New images

New atlas

New image and atlas

See README


32/49


Template Builder Tutorial

EMSegment tutorial data is required Within Slicer3, open EMSegment Module


33/49



Load tutorial parameters File->Load Scene

Select mrml file includedwith tutorial

Explore volumes includedwith scene Target volumes, atlas

volumes

An old segmentation resultis also included


34/49



Step 1: ChooseEMSegmenterparameter set

For now, use tutorial set Later use this interface

to create a newparameter set


35/49



Define anatomicalstructures

Right click to add or

delete nodes Label corresponds to

eventual voxel values insegmentation result


36/49



Choose an atlas volumefor each leaf node

To change or add new

atlas volumes Load new volumes into

Slicer3

Select new volumes at

this step


37/49


38/49



You can choose toalign target images

First target is fixed

image Rigid, mutual

informationregistration


39/49



You can choose tonormalize target images

Simple, default strategy

Default parameter setsavailable from pulldown


40/49



Intensity distributions defineappearance of each leafstructure

Gaussian

Dimensionality equal tonumber of target images Two methods

Sample voxels from images Specify mean and covariance

manually

Tip: sample first, then finetune manually


41/49



Sample voxels fromimages

Load first target image

into Slicer3 slicer view Choose anatomical

structure

Choose `manual

sampling Ctrl-left-click on image

to add voxels


42/49



Sample voxels fromimages (continued)

To remove an unwanted

sample right-click on it andchoose remove

Change back to manual

mode to tweak distribution


43/49



Segmentationparameters for every treenode

Influence Prior weight relative toother structures

Atlas

Input channels

Smoothing (parent nodesonly)

Stopping Conditions


44/49



Atlas-to-target registrationparameters

Moving image registered tofirst target image

You can choose any imageloaded into Slicer3

Same transformation appliedto all atlas images before

segmentation begins


45/49



Select output and runregistration

You can troubleshootpreprocessing by savingintermediate results

Choose an output labelmap

ROI governs segmentationprocessing (one-based, notzero-based)


46/49


Acknowledgements

Wendy Plesniak, SPL BWH

Steve Pieper, Isomics

Luis Ibez, Kitware Sylvain Bouix, PNL BWH

William Wells, BWH

Funding provided by NAMIC


47/49

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

Questions

Sli 3 EM S


48/49


SpecifyInputs

DefaultPre-

Processing


Target ImageNormalization

Target-to-targetRegistration

Atlas-to-targetRegistration


ReviewResults

Slicer3 Slice

Views

Slicer3 Model

Maker

External

Program


Parameters


49/49

National Alliance for Medical Image Computing

Atlas Images

Target Images

Parameters

Segmentation Result

SpecifyInputs

DefaultPre-

Processing


Target ImageNormalization

Target-to-targetRegistration

Atlas-to-targetRegistration


ReviewResults

Slicer3 Slice

Views

Slicer3 Model

Maker

External

Program

emsegtutorial-ahm2008

Documents