thesis, image registration methods
TRANSCRIPT
Image Registration Methods for Reconstructing a Gene Expression
Atlas of Early Zebrafish Embryogenesis
Evangelia BalanouMASTER THESIS
EUROPEAN POSTGRADUATE PROGRAM ON BIOMEDICAL ENGINEERINGUNIVERSITY OF PATRAS – NATIONAL TECHNICAL UNIVERSITY OF ATHENS
DEPARTMENT OF ELECTRONIC ENGINEERING TECHNICAL SCHOOL OF TELECOMMUNICATIONS ENGINEERING
TECHNICAL UNIVERSITY OF MADRID
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
• Introduction– Motivation– Problem– Goal
• Image Registration– Components
• Design and Implementation– Concept– Overview– Registration Pipeline– Atlas Construction Pipeline– Tools– Implementation
• Results and Evaluation– Comparison of Registration Methods– Atlas Construction
• Conclusions and Future Work
Outline
IntroductionMotivation
Problem to be solved
Motivation
Early development of a zebrafish embryo
• Study the genes that regulate embryonic development (developmental biology)
• Study embryonic development of vertebrates:– Vertebrate developmental disorders– Human hereditary disease
• Vertebrate model: zebrafish– Rapidly developing transparent embryos– Small size (4-5 cm length)– Short generation time
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
• Quantitative spatio-temporal data at cellular level about gene expression required
Provided by Fluorescence In Situ Hybridization techniques and Laser Scanning Microscopy
Problem
z
x
y
Second gene expression pattern
One gene expression pattern
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
• Qualitative spatio-temporal data at cellular level about gene expression required
Provided by Fluorescence In Situ Hybridization techniques and Laser Scanning Microscopy
Problem
• However, not more than five gene expression patterns simultaneously revealed on the same embryo!
Image processing methods to integrate different expression patterns (from different embryos) into a 3-D gene expression atlas
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
GoalDesign and implement an image processing framework able to register different datasets with different gene expression patterns to a common template at a given developmental stage
Template One datasetTemplate + registered image
“Registration is the process of determining a geometrical transformation that aligns points in one view of an object with corresponding points in another view of that object or another object.”
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Image RegistrationFundamental task in image processingVarious techniques (data, application)
Image Registration• Intensity-based :
Calculates the transformation using voxel values alone
• Input: 2 images – fixed, moving Output: geometrical transformation
• Optimization problem
• Decomposed into a set of basic elements (defining different methods)
Optimization
Transformation
Resampling
Fixedimage
Movingimage
Registeredimage
TransformationParameters
Registration
Similarity measure
Interpolation
Initial Parameters
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Transformation
Optimization
Transformation
Resampling
Fixedimage
Movingimage
Registeredimage
TransformationParameters
Registration
Similarity measure
Interpolation
Initial Parameters
• Defines the type of parameters whose values align the two images (search space)
• Spatial mapping of points from the fixed image space to points in the moving image space (inverse mapping)
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Interpolation
Optimization
Transformation
Resampling
Fixedimage
Movingimage
Registeredimage
TransformationParameters
Similarity measure
Interpolation
Initial Parameters
• Evaluate moving image intensities at the mapped, non-grid positions
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Similarity Measure
Optimization
Transformation
Resampling
Fixedimage
Movingimage
Registeredimage
TransformationParameters
Similarity measure
Interpolation
Initial Parameters
• A measure of “how well” fixed and transformed moving match each other• Provides a quantitative criterion to be optimized over the search space
(similarity measure function, S(T) )• The desired optimum may be one of the local ones
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
• Correlation Coefficient
Similarity Measures
• Mutual Information
Intensities in two images linearly related As written, function to be maximized
Intensities in two images statistically related As written, function to be maximized
Fixe
d Im
age
inte
nsity
(I2)
Moving Image intensity (I1)
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Fixe
d Im
age
inte
nsity
(I2)
Moving Image intensity (I1)
Optimization
Optimization
Transformation
Resampling
Fixedimage
Movingimage
Registeredimage
TransformationParameters
Similarity measure
Interpolation
Initial Parameters
• Most complex component• Starting from an initial set of parameters, iteratively searches the optimal
solution of the similarity measure function over the parameter space defined by the transformation
• Stops when stopping criterion is met
Transformation Parameters
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Optimization Algorithms• Gradient Descent
• Differential Evolution
Derivative of similarity measure function (S) wrt to each transformation parameterAttracted by local extrema
Stochastic, population-basedGlobal optimization technique – slow in computation
n
nn pSpp
1
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Initialization Mutation Recombination Selection
Cost FunctionOutline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Local optimization
Start
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Local optimization
End
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Global optimization
Start
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Global optimization
End
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work Capture range of correct optimum (initial parameter range or initialization)
Resampling
Optimization
Transformation
Resampling
Fixedimage
Movingimage
Registeredimage
TransformationParameters
Similarity measure
Interpolation
Initial Parameters
• Once a stopping criterion is met or iteration number has reached, the last transformation parameters are used to produce the registered image
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Design & ImplementationImplemented framework’s concept
Different steps it is composed of
Concept
Template embryoPartial views
Nuclei channel
Reference gene channel(goosecoid)
Another gene channel
*All images are 3D and grayscale *Colourmap just for visualization
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Goal: Design and implement an image processing framework able to register different datasets with different gene expression patterns to a common template at a given developmental stage
ConceptTemplate embryoPartial views
Registration
Gene expression atlas
Nuclei channel
Reference gene channel
Nuclei channel
Reference gene channel
Another gene channel
Partial view of another embryo
*All images are 3D and grayscale *Reference gene (position): goosecoid (gsc)*Colourmap just for visualization
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Overview
Moving image
Whole embryo view, nuclei channel
Whole embryo view, gsc channel
Partial embryo view, nuclei channel
registration
Transformation Parameters
Registered image
Partial embryo view, gsc channel
Fixed image
addition
addition
initialization
Fixed image
Initialized Moving image
preprocessing
preprocessing
preprocessing
preprocessing
Rotation centre
Partial embryo view, third channel
preprocessing transformation Third channel mapped
Registration pipeline
Atlas construction pipeline
initialization
Gravity centres
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Registration Pipeline
Moving image
Whole embryo view, nuclei channel
Whole embryo view, gsc channel
Partial embryo view, nuclei channel
registration
Transformation Parameters
Registered image
Partial embryo view, gsc channel
Fixed image
addition
addition
initialization
Fixed image
Initialized Moving image
preprocessing
preprocessing
preprocessing
preprocessing
Rotation centre
Partial embryo view, third channel
preprocessing transformation Third channel mapped
Registration pipeline
Atlas construction pipeline
initialization
Gravity centres
Purpose: Determine the transformation parameters that bring into spatial alignment the template and one partial view
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Preprocessing & Addition Step
Moving image
Whole embryo view, nuclei channel
Whole embryo view, gsc channel
Partial embryo view, nuclei channel
registration
Transformation Parameters
Registered image
Partial embryo view, gsc channel
Fixed image
addition
addition
initialization
Fixed image
Initialized Moving image
preprocessing
preprocessing
preprocessing
preprocessing
Rotation centre
Registration pipeline
Purpose: Remove noise, blur, downsample, thresholdCombine information from nuclei and gsc channels into a single image
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Preprocessing & Addition Step
• Preprocessing depends on images (noise, size)• Weighted Addition
additionpreprocessing
preprocessingOriginal gsc channel
Original nuclei channel Combined
image
2550
Original channels Preprocessed channels Combined image
addition
preprocessing
preprocessing
Resolution: 512 x 512 x 465Voxel size: 1.517 x 1.517 x 1,509μm
Resolution: 128 x 128 x 116Voxel size: 6.068 x 6.068 x 6.036μm
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Initialization Step
Moving image
Whole embryo view, nuclei channel
Whole embryo view, gsc channel
Partial embryo view, nuclei channel
registration
Transformation Parameters
Registered image
Partial embryo view, gsc channel
Fixed image
addition
addition
initialization
Fixed image
Initialized Moving image
preprocessing
preprocessing
preprocessing
preprocessing
Rotation centre
Registration pipeline
Purpose: Initial positioning of moving to fixed image’s space (no initial parameters in registration)
If NOT sufficient overlapping, registration fails
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
• Based on nature of data (nuclei and gsc channel)
• For both views one gravity centre from each channel
• The resulting four points define a spatial transformation that is applied on the moving image
Initialization StepPreprocessed partial embryo view, nuclei channel (binary)
Preprocessed partial embryo view, gsc channel
Initialized Moving image
Rotation centre
Moving image
Fixed image
initialization
Preprocessed whole embryo view, nuclei channel (binary)
Preprocessed whole embryo view, gsc channel
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Initialization Step
x
y
z
gscfixed
nfixed
nmoving
gscmoving
Translated nmoving
Rotation axis
Rotation angle
translation
vF
vM
nfixed
gscfixed
nmoving
gscmoving
Fixed (template view)
Moving (partial view)
*Blue/Orange-nuclei Green/Yellow-gsc expression pattern
Translated nmoving
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Initialization Step
Before initialization
After Initialization
Fixed (template) + Initialized Moving (partial) Partial view before and after initialization
Initialized Moving
Fixed Image
*Blue/Orange-nuclei Green/Yellow-gsc expression pattern
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Registration Step
Moving image
Whole embryo view, nuclei channel
Whole embryo view, gsc channel
Partial embryo view, nuclei channel
registration
Transformation Parameters
Registered image
Partial embryo view, gsc channel
Fixed image
addition
addition
initialization
Fixed image
Initialized Moving image
preprocessing
preprocessing
preprocessing
preprocessing
Rotation centre
Registration pipeline
Purpose: Find the transformation parameters that register the initialized moving image to the fixed
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Registration StepOutline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Global, Rigid 3D Transformation
Resampling
Fixed image
Initialized Movingimage
Registeredimage
TransformationParameters
Implemented Registration step
Trilinear Interpolation
Initial Parameters (rotation centre)
Mutual Information
Correlation Coefficient
Gradient Descent
Differential Evolution
or
or
• Global, rigid transformation-> Assumption: embryos similar in size and shape
-> 3 rotations + 3 translation = 6 transformation parameters
• 2 similarity measures and 2 optimization algorithms
Registration Step
Initialized Moving
Fixed Image
Fixed (template) + Initialized Moving (partial)
Fixed Image
Registered Image
Fixed (template) + Registered Image
*Blue/Orange-nuclei Green/Yellow-gsc expression pattern
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Atlas Construction PipelineMoving image
Whole embryo view, nuclei channel
Whole embryo view, gsc channel
Partial embryo view, nuclei channel
registration
Transformation Parameters
Registered image
Partial embryo view, gsc channel
Fixed image
addition
addition
initialization
Fixed image
Initialized Moving image
preprocessing
preprocessing
preprocessing
preprocessing
Rotation centre
Partial embryo view, third channel
preprocessing transformation Third channel mapped
Registration pipeline
Atlas construction pipeline
initialization
Gravity centres
Purpose: Transformation of the third channel of the partial viewOnly transformation step is implemented as a new program
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Atlas Construction Pipeline
Registration Pipeline -> Transformation Parameters
Partial view White-nucleiRed-gsc expression patternGreen-snail expression pattern
TemplateOrange-nucleiYellow-gsc expression pattern
Atlas Construction Pipeline -> Apply Transformation Parameters
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Development• Insight Segmentation and Registration Toolkit
– Available at www.itk.org• CMake
– Available at www.cmake.org • Microsoft Visual Studio 2008
Tools
Visualization• Amide– Available at http://amide.sourceforge.net/
• Amira– Commercial product
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Implementation
• User’s manual provided• Run from command line configuring parameters
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Results & Evaluation
• Developmental stage: Shield (6 hpf)
• Framework tested with six datasets (six embryos)
– One template, one whole embryo view
– Partial views of five different embryos
Data
Dorsal
Animal
Ventral
Vegetal
* Images provided by: DEPSN , France
nuclei channel
gsc channel
co-stained gene expression pattern e.g. snail
Template embryoPartial view
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Questions
1. Does the implemented framework succeed in registering our data?
2. What is the combination of similarity measure and optimization algorithm that results in a successful registration?
In other words…
What is the most appropriate registration method for our application?
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Preprocessing & Addition
• Framework works with 2 datasets each time• Preprocessing: smoothed, downsampled, nuclei channel turned to binary• Addition: nuclei and gsc channels combined into a single image• 5 partial -> 5 iterations (6 images in total – 1 fixed, 5 moving)
addition
preprocessing
preprocessing
preprocessing
preprocessing
addition
Original channels Preprocessed channels Combined imageTe
mpl
ate
One
par
tial V
iew
Fixed image
One moving image
Slice Volume rendering
Slice Volume rendering
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Initialization
Initialization looks promising…
Template + Initialized partial 1 Template + Initialized partial 2 Template + Initialized partial 3
Template + Initialized partial 4 Template + Initialized partial 5
*Blue/Orange-nuclei Green/Yellow-gsc expression pattern
Initialized Partial 1
Template ImageInitialized Partial 2
Template Image Initialized Partial 3
Template Image
Initialized Partial4
Template Image
Initialized Partial 5
Template Image
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Method Evaluation• Four different methods implemented
• Evaluation only by visual inspection of the results
– Optimization algorithms not comparable unless running with optimized parameters
– Lack of golden standard
– Point-to-point correspondence does not exist (different embryos)
Similarity measures Optimization algorithmsCorrelation Coefficient Gradient Descent
Mutual Information Differential Evolution
Outline
IntroductionMotivation
Problem
Goal
ImageRegistrationTransformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
OverviewRegistration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration MethodsAtlas
Conclusions & Future Work
Method EvaluationOptimization algorithm
Similarity measure Gradient descent Differential evolution
Correlation Coefficient
Mutual Information
*After 100 iterations
• Monomodal case, intensities are linearly related (C.C. ideal)• Global optimization algorithm is still computing (D.E. not suitable)• Initialization sufficient (Gradient Descent is “myopic”)
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
• Transformation parameters taken from the registration method with the most coherent performance and successful results
– Correlation Coefficient optimized by the Gradient Descent algorithm
• Initialization parameters (initialization) and transformation parameters (registration) applied on the third channel of three datasets
Atlas Construction
Original view
After mappingOriginal view
After mapping
Original view
After mapping
gsc - spt gsc - snail gsc - chd
chd’s expressionsnail's expression
spt’s expression gsc’s expression
Partial view 3Volume rendering of gsc , third channel, registered gsc and transformed third channel
Partial view 4Volume rendering of gsc , third channel, registered gsc and transformed third channel
Partial view 5Volume rendering of gsc , third channel, registered gsc and transformed third channel
Outline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Conclusions & Future work
• Goal achievedDesigned and implemented an image processing framework able to map different gene expression patterns on a common template (for a given developmental stage)
• Key points– Addition: Combine information from two channels– Initialization: Solves the problem of capture range for
optimization– Registration Method: Correlation Coefficient + Gradient
Descent
Summary - ConclusionsOutline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
• Advantages
– Modularity
– Configurability
– Semi-automated
• Future work
– More datasets -> more gene expression patterns
– Other developmental stages
– Validated with known gene regulatory networks
Conclusions-Future workOutline
Introduction
Motivation
Problem
Goal
ImageRegistration
Transformation
Interpolation
Similarity Measure
Optimization
Resampling
Design & Implementation
Concept
Overview
Registration Pipeline
Atlas Construction Pipeline
Tools
Implementation
Results & Evaluation
Comparison of Registration Methods
Atlas
Conclusions & Future Work
Thanks to…
Biomedical Image Technologies Laboratory (BIT)Technical School Of Telecommunications Engineering (ETSIT)
Technical University of Madrid (UPM)
Thank you for your attention