Thesis, Image Registration Methods

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  • Present yourself
  • Movie (24 hpf):Zygote, cleavage (volume constant), blastula (MBT, epiboly), gastrula (germ layers formed, involves complex cell movements, shield D-V axis), segmentation (first cells differentiate morphologically)
  • Tools for studying the spatial characteristics of gene expression FISH + microscopyStained by FISH and imaged by.Imaging: the only way to record spatial location ++structural infoScanning across x+y to acquire optical sliceCan be visualized in projections (slices) or volume rendering! THEY R VOLUMETRIC IMAGES!! to view gene activity=> FISH, attaching a probe to a transcript!!FISH: the assay of choice for localization of specific nucleic acids sequences in native context. Basic principles remain unchanged, now a wide spectrum of detection schemesemission independent of absorption!! Here spectrum with Leica!“capture the relative spatial context of the fluorescently labelled structures”“spatial map of gene expression patterns”Definition of the channel: “ the image data from each fluorescent label” the two or three channels were acquired separately but simultaneously, as the emission spectrum is distinct.The dyes emit light in different parts of the spectrum, so that three separate images of the embryo can be collected with the appropriate color filters (middle)
  • Tools for studying the spatial characteristics of gene expression FISH + microscopyStained by FISH and imaged by.Imaging: the only way to record spatial location ++structural infoScanning across x+y to acquire optical sliceCan be visualized in projections (slices) or volume rendering! THEY R VOLUMETRIC IMAGES!! to view gene activity=> FISH, attaching a probe to a transcript!!FISH: the assay of choice for localization of specific nucleic acids sequences in native context. Basic principles remain unchanged, now a wide spectrum of detection schemesemission independent of absorption!! Here spectrum with Leica!“capture the relative spatial context of the fluorescently labelled structures”“spatial map of gene expression patterns”Definition of the channel: “ the image data from each fluorescent label” the two or three channels were acquired separately but simultaneously, as the emission spectrum is distinct.The dyes emit light in different parts of the spectrum, so that three separate images of the embryo can be collected with the appropriate color filters (middle)
  • Image registration is the process of determining the spatial transform that maps points from one image to homologous points in another.
  • We r talking about the intensity based approach that works directly on the intensity values of the images and does not require any interaction from the user (while the registration is running).The basic input data are two images: one defined as fixed (static) and the other as moving, that will be spatially map to align with the first.Treated as an optimization problem with the goal of finding the spatial mapping that will bring the MOVING into alignment with the FIXED.
  • We would expect the transformation parameters to map points from the moving to fixed. However this transformation could result in holes or overlaps. Therefore the transformation is done backwards.Inverse mapping= avoid holes, overlaps
  • The intensities on the transformed grid are taken by interpolating values in the moving.
  • After the transformation, the images are run thru a similarity measureFor large transformations (total misalignment) only background noise overlaps!The optimum of this function is assumed to correspond to the transformation that successfully registers the images
  • Based on the Information Theory, that says that the amount of information they contain about each other is maximal.H entropies information they contain about themselves, joint entropy measures the dispersion of the joint probability distribution. The more they match, the clearer the clusters that can be seen on the joint histogram.
  • The result of the similarity measure is given to ..Goal: is the component that drives the registration. It explores the parameter space of the T in search of a set of values that optimize the similarity measure function!!!!! This is an iterative procedure until reaches…Now a question here is whether we are looking for global or local of the similarity measure function. For intensity-based registration measures, it is possible that a large misregistration of two images results in a better value of the measure than the correct transformation. The desired optimum may not be the global one of the search space and only part of the search space leads to the desired optimum.
  • GD: Advances parameters in the direction of the gradient where the step size is governed by a learning rate (λ)DE: It is an evolutionary algorithm. Starting from a population vector with size NP, it generates a mutant vector from the existing elements (f is just a weighting factor). To increase diversity of the population, crossing-over with a probability of CR is introduced to construct a trial vector. This trial vector is compared to the population vector and the elements that yield the best similarity measure values are passed on to the next generation (greedy criterion).(Gradient: we can choose the directionPopulation vector initial parameter values randomly from IPR, parameters for next generation vector selected according to the greedy criterion)One difference between them is that GD requires derivative of S whereas DE no!! Another is local, global
  • That is function to be minimized…
  • Stochastic, population-based
  • Stochastic, population-based
  • Stochastic, population-based
  • Start the algorithm within the capture range of the desired optimum!Starting estimate of transformation close to the correct solution (initialization)
  • The output is the transformation. That is given to ..Last step of the registration is to use the resulting transformation to map the moving image onto the fixed image SPACE!!Takes moving and parameters and produces the registered transformed image.
  • Present yourself
  • Because cellular resolution was required for this dataset, they cover limited views, restricted to the stained region.
  • The pipeline that prepares the data before the actual registration, the pipeline that leads all the way to registration
  • scalar parameter A balances the weight in the registrationprocess of nuclei structural information and gene expression details
  • the better the images overlap before the registration step, the less displacement theregistration algorithm has to cover and more chances to obtain a useful alignment.
  • We have already seen the components it is composed of. Here we can see what has been selected for each component.2 and 2 so we can compare their performance.
  • Command line programsThe parameters under which each program is run can be configured by the user!!!!
  • Present yourself
  • “the volume that has been imaged encompasses part of the blastoderm (embryonic cell mass?) and does not get into the yolk (non cellular mass of nutrients)”DEPSN (Development Evolution Plasticity of the Nervous System), Francedatasets dapi, CY5, FITC, imaged by LSM
  • Many qs concerning configuration of parameters of programs.
  • Template always enters the algorithmNext comes the step of initialization
  • (part of the transformation parameters space that includes the desired optimal value
  • the combination of the correlation coefficient with the gradient descent algorithm presented a coherent and sufficient performance, when the division factor in the addition step was in the [1–6] rangeAlthough the in situ hybridizations of gene X and Y were conducted independently, their expression patterns can be visualized simultaneously.
  • Present yourself
  • End!
  • Thesis, Image Registration Methods

    1. 1. Image Registration Methods for Reconstructing a Gene Expression Atlas of Early Zebrafish Embryogenesis<br />Department Of Electronic Engineering <br />Technical School Of Telecommunications Engineering<br />Technical University Of Madrid<br />Evangelia Balanou<br />Master Thesis<br />European Postgraduate Program On Biomedical Engineering<br />University of Patras – National Technical University of Athens<br />
    2. 2. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />Image Registration<br />Components<br />Design and Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results and Evaluation<br />Comparison of Registration Methods<br />Atlas Construction<br />Conclusions and Future Work<br />
    3. 3. Introduction<br />Motivation<br />Problem to be solved <br />
    4. 4. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Motivation<br /><ul><li>Study the genes that regulate embryonic development (developmental biology)
    5. 5. Study embryonic development of vertebrates:
    6. 6. Vertebrate developmental disorders
    7. 7. Human hereditary disease
    8. 8. Vertebrate model: zebrafish
    9. 9. Rapidly developing transparent </li></ul> embryos<br /><ul><li>Small size (4-5 cm length)
    10. 10. Short generation time</li></ul>Early development of a zebrafish embryo<br />
    11. 11. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Problem<br />Quantitative spatio-temporal data at cellular level about gene expression required<br /> Provided by <br />Fluorescence In Situ Hybridization techniques and <br />Laser Scanning Microscopy<br />Second gene expression pattern<br />x<br />One gene expression pattern<br />y<br />z<br />
    12. 12. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Problem<br />Qualitative spatio-temporal data at cellular level about gene expression required<br /> Provided by <br />Fluorescence In Situ Hybridization techniques and <br />Laser Scanning Microscopy<br /><ul><li>However, not more than five gene expression patterns simultaneously revealed on the same embryo!</li></ul>Image processing methods to integrate different expression patterns (from different embryos) into a 3-D gene expression atlas<br />
    13. 13. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Goal<br />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<br />“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.”<br />Template<br />One dataset<br />Template + registered image<br />
    14. 14. Image Registration<br />Fundamental task in image processing<br />Various techniques (data, application)<br />
    15. 15. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Image Registration<br />Intensity-based :<br /> Calculates the transformation using voxel values alone<br />Input: 2 images – fixed, moving<br /> Output: geometrical transformation<br />Optimization problem<br />Decomposed into a set of basic elements (defining different methods)<br />Registration<br />Similarity measure<br />Fixedimage<br />TransformationParameters<br />Movingimage<br />Interpolation<br />Optimization<br />Resampling<br />Registeredimage<br />Transformation<br />Initial Parameters<br />
    16. 16. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Transformation<br />Registration<br />Similarity measure<br />Fixedimage<br />TransformationParameters<br />Movingimage<br />Interpolation<br />Optimization<br />Resampling<br />Registeredimage<br />Transformation<br />Initial Parameters<br />Defines the type of parameters whose values align the two images (search space)<br />Spatial mapping of points from the fixed image space to points in the moving image space (inverse mapping)<br />
    17. 17. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Interpolation<br />Similarity measure<br />Fixedimage<br />TransformationParameters<br />Movingimage<br />Interpolation<br />Optimization<br />Resampling<br />Registeredimage<br />Transformation<br />Initial Parameters<br />Evaluate moving image intensities at the mapped, non-grid positions<br />
    18. 18. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Similarity Measure<br />Similarity measure<br />Fixedimage<br />TransformationParameters<br />Movingimage<br />Interpolation<br />Optimization<br />Resampling<br />Registeredimage<br />Transformation<br />Initial Parameters<br />A measure of “how well” fixed and transformed moving match each other<br />Provides a quantitative criterion to be optimized over the search space (similarity measure function, S(T) )<br />The desired optimum may be one of the local ones<br />
    19. 19. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Similarity Measures<br />Correlation Coefficient <br />Fixed Image intensity (I2)<br />Moving Image intensity (I1)<br />Intensities in two images linearly related <br />As written, function to be maximized<br /><ul><li>Mutual Information</li></ul>Fixed Image intensity (I2)<br />Intensities in two images statistically related <br />As written, function to be maximized <br />Moving Image intensity (I1)<br />
    20. 20. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Optimization<br />Similarity measure<br />Fixedimage<br />TransformationParameters<br />Movingimage<br />Interpolation<br />Optimization<br />Resampling<br />Registeredimage<br />Transformation Parameters<br />Transformation<br />Initial Parameters<br />Most complex component<br />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<br />Stops when stopping criterion is met<br />
    21. 21. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Optimization Algorithms<br /><ul><li>Gradient Descent</li></ul>Derivative of similarity measure function (S)wrt to each transformation parameter<br />Attracted by local extrema<br /><ul><li>Differential Evolution</li></ul>Initialization<br />Mutation<br />Recombination<br />Selection<br />Stochastic, population-based<br />Global optimization technique – slow in computation<br />
    22. 22. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Cost Function<br />
    23. 23. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Local optimization<br />Start<br />
    24. 24. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Local optimization<br />End<br />
    25. 25. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Global optimization<br />Start<br />
    26. 26. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Global optimization<br />End<br />Capture range of correct optimum (initial parameter range or initialization)<br />
    27. 27. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Resampling<br />Similarity measure<br />Fixedimage<br />TransformationParameters<br />Movingimage<br />Interpolation<br />Optimization<br />Resampling<br />Registeredimage<br />Transformation<br />Initial Parameters<br />Once a stopping criterion is met or iteration number has reached, the last transformation parameters are used to produce the registered image<br />
    28. 28. Design & Implementation<br />Implemented framework’s concept<br />Different steps it is composed of<br />
    29. 29. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Concept<br />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<br />Partial views<br />Template embryo<br />Nuclei channel<br />Reference gene channel<br />(goosecoid)<br />Another gene channel<br />*All images are 3D and grayscale<br />*Colourmap just for visualization<br />
    30. 30. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Concept<br />Partial view of another embryo<br />Partial views<br />Template embryo<br />Nuclei channel<br />Nuclei channel<br />Registration<br />Reference gene channel<br />Reference gene channel<br />Another gene channel<br />*All images are 3D and grayscale<br />*Reference gene (position): goosecoid (gsc)<br />*Colourmap just for visualization<br />Gene expression atlas<br />
    31. 31. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Overview<br />registration<br />addition<br />initialization<br />Partial embryo view, nuclei channel<br />Initialized Moving image<br />preprocessing<br />Moving image<br />Partial embryo view, gsc channel<br />preprocessing<br />Registered <br />image<br />Rotation centre<br />Transformation <br />Parameters <br />Whole embryo view, nuclei channel<br />addition<br />preprocessing<br />Fixedimage<br />Fixedimage<br />Whole embryo view, gsc channel<br />preprocessing<br />Gravity centres<br />Registration pipeline<br />initialization<br />Partial embryo view, third channel<br />preprocessing<br />transformation<br />Third channel <br />mapped<br />Atlas construction pipeline<br />
    32. 32. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Registration Pipeline<br />registration<br />addition<br />initialization<br />Partial embryo view, nuclei channel<br />Initialized Moving image<br />preprocessing<br />Moving image<br />Partial embryo view, gsc channel<br />preprocessing<br />Registered <br />image<br />Rotation centre<br />Transformation <br />Parameters <br />Whole embryo view, nuclei channel<br />addition<br />preprocessing<br />Fixedimage<br />Fixedimage<br />Whole embryo view, gsc channel<br />preprocessing<br />Gravity centres<br />Registration pipeline<br />initialization<br />Partial embryo view, third channel<br />preprocessing<br />transformation<br />Third channel <br />mapped<br />Atlas construction pipeline<br />Purpose: Determine the transformation parameters that bring into spatial alignment the template and one partial view<br />
    33. 33. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Preprocessing & Addition Step <br />registration<br />addition<br />initialization<br />Partial embryo view, nuclei channel<br />Initialized Moving image<br />preprocessing<br />Moving image<br />Partial embryo view, gsc channel<br />preprocessing<br />Registered <br />image<br />Rotation centre<br />Transformation <br />Parameters <br />Whole embryo view, nuclei channel<br />addition<br />preprocessing<br />Fixedimage<br />Fixedimage<br />Whole embryo view, gsc channel<br />preprocessing<br />Registration pipeline<br />Purpose: Remove noise, blur, downsample, threshold<br />Combine information from nuclei and gsc channels into a single image<br />
    34. 34. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Preprocessing & Addition Step <br />addition<br />preprocessing<br />Original nuclei channel<br />Combined image<br />Original gsc channel<br />preprocessing<br /><ul><li>Preprocessing depends on images (noise, size)
    35. 35. Weighted Addition </li></ul>addition<br />preprocessing<br />preprocessing<br />0<br />255<br />Combined image<br />Original channels <br />Preprocessed channels<br />Resolution: 512 x 512 x 465<br />Voxel size: 1.517 x 1.517 x 1,509μm<br />Resolution: 128 x 128 x 116<br />Voxel size: 6.068 x 6.068 x 6.036μm<br />
    36. 36. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Initialization Step<br />registration<br />addition<br />initialization<br />Partial embryo view, nuclei channel<br />Initialized Moving image<br />preprocessing<br />Moving image<br />Partial embryo view, gsc channel<br />preprocessing<br />Registered <br />image<br />Rotation centre<br />Transformation <br />Parameters <br />Whole embryo view, nuclei channel<br />addition<br />preprocessing<br />Fixedimage<br />Fixedimage<br />Whole embryo view, gsc channel<br />preprocessing<br />Registration pipeline<br />Purpose: Initial positioning of moving to fixed image’s space <br /> (no initial parameters in registration) <br /> If NOT sufficient overlapping, registration fails<br />
    37. 37. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Initialization Step<br />Preprocessed partial embryo view, nuclei channel (binary)<br />Preprocessed partial embryo view, gsc channel<br />initialization<br />Moving image<br />Initialized Moving image<br />Rotation centre<br />Fixedimage<br />Preprocessed whole embryo view, nuclei channel (binary)<br />Preprocessed whole embryo view, gsc channel<br />Based on nature of data (nuclei and gsc channel)<br />For both views one gravity centre from each channel <br />The resulting four points define a spatial transformation that is applied on the moving image <br />
    38. 38. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Initialization Step<br />nmoving<br />gscmoving<br />y<br />gscmoving<br />Rotation axis<br />nfixed<br />vF<br />nmoving<br />translation<br />Moving (partial view)<br />Rotation angle<br />gscfixed<br />Translated nmoving<br />vM<br />Translated nmoving<br />nfixed<br />x<br />z<br />gscfixed<br />*Blue/Orange-nuclei<br /> Green/Yellow-gsc expression pattern<br />Fixed (template view)<br />
    39. 39. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Initialization Step<br />Before initialization<br />Fixed Image<br />Initialized Moving<br />After Initialization<br />Fixed (template) + Initialized Moving (partial)<br />Partial view before and after initialization<br />*Blue/Orange-nuclei<br /> Green/Yellow-gsc expression pattern<br />
    40. 40. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Registration Step<br />registration<br />addition<br />initialization<br />Partial embryo view, nuclei channel<br />Initialized Moving image<br />preprocessing<br />Moving image<br />Partial embryo view, gsc channel<br />preprocessing<br />Registered <br />image<br />Rotation centre<br />Transformation <br />Parameters <br />Whole embryo view, nuclei channel<br />addition<br />preprocessing<br />Fixedimage<br />Fixedimage<br />Whole embryo view, gsc channel<br />preprocessing<br />Registration pipeline<br />Purpose: Find the transformation parameters that register the initialized moving image to the fixed<br />
    41. 41. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Registration Step<br />Implemented Registration step<br />or<br />Correlation Coefficient<br />Fixed image<br />TransformationParameters<br />Mutual Information<br />or<br />Differential Evolution<br />Resampling<br />Initialized Movingimage<br />Trilinear Interpolation<br />Gradient Descent<br />Registeredimage<br />Global, Rigid 3D Transformation<br />Initial Parameters (rotation centre)<br /><ul><li>Global, rigid transformation</li></ul>-> Assumption: embryos similar in size and shape<br />-> 3 rotations + 3 translation = 6 transformation parameters<br /><ul><li>2 similarity measures and 2 optimization algorithms</li></li></ul><li>Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Registration Step<br />Fixed Image<br />Fixed Image<br />Initialized Moving<br />Registered Image<br />Fixed (template) + Initialized Moving (partial)<br />Fixed (template) + Registered Image<br />*Blue/Orange-nuclei<br /> Green/Yellow-gsc expression pattern<br />
    42. 42. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Atlas Construction Pipeline<br />registration<br />addition<br />initialization<br />Partial embryo view, nuclei channel<br />Initialized Moving image<br />preprocessing<br />Moving image<br />Partial embryo view, gsc channel<br />preprocessing<br />Registered <br />image<br />Rotation centre<br />Transformation <br />Parameters <br />Whole embryo view, nuclei channel<br />addition<br />preprocessing<br />Fixedimage<br />Fixedimage<br />Whole embryo view, gsc channel<br />preprocessing<br />Gravity centres<br />Registration pipeline<br />initialization<br />Partial embryo view, third channel<br />preprocessing<br />transformation<br />Third channel <br />mapped<br />Atlas construction pipeline<br />Purpose: Transformation of the third channel of the partial view<br /> Only transformation step is implemented as a new program<br />
    43. 43. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Atlas Construction Pipeline<br />Atlas Construction Pipeline <br /> -> Apply Transformation Parameters<br />Partial view <br />White-nuclei<br />Red-gsc expression pattern<br />Green-snail expression pattern<br />Template<br />Orange-nuclei<br />Yellow-gsc expression pattern<br />Registration Pipeline <br /> -> Transformation Parameters<br />
    44. 44. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Tools<br />Development<br />Insight Segmentation and Registration Toolkit<br />Available at www.itk.org<br />CMake<br />Available at www.cmake.org<br />Microsoft Visual Studio 2008<br />Visualization<br /><ul><li>Amide
    45. 45. Available at http://amide.sourceforge.net/
    46. 46. Amira
    47. 47. Commercial product</li></li></ul><li>Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Implementation<br />User’s manual provided<br />Run from command line configuring parameters<br />
    48. 48. Results & Evaluation<br />
    49. 49. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Data<br />Developmental stage: Shield (6 hpf)<br />Framework tested with six datasets (six embryos)<br />One template, one whole embryo view<br />Partial views of five different embryos<br />Animal<br />Dorsal<br />Ventral<br />Vegetal<br />Template embryo<br />Partial view<br />nuclei channel<br />gsc channel<br />co-stained gene expression pattern e.g. snail<br />* Images provided by: DEPSN , France<br />
    50. 50. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Questions<br />Does the implemented framework succeed in registering our data?<br />What is the combination of similarity measure and optimization algorithm that results in a successful registration?<br />In other words…<br />What is the most appropriate registration method for our application?<br />
    51. 51. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Preprocessing & Addition<br />Original channels <br />Preprocessed channels<br />Combined image<br />preprocessing<br />addition<br />Template<br />preprocessing<br />Slice<br />Volume rendering<br />Fixed image<br />addition<br />preprocessing<br />One partial View<br />preprocessing<br />Slice<br />Volume rendering<br />One moving image<br />Framework works with 2 datasets each time<br />Preprocessing: smoothed, downsampled, nuclei channel turned to binary<br />Addition: nuclei and gsc channels combined into a single image<br />5 partial -> 5 iterations (6 images in total – 1 fixed, 5 moving)<br />
    52. 52. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Initialization<br />Template Image<br />Template Image<br />Template Image<br />Initialized Partial 2<br />Initialized Partial 3<br />Initialized Partial 1<br />Template + Initialized partial 1<br />Template + Initialized partial 2<br />Template + Initialized partial 3<br />Template Image<br />Template Image<br />Initialized Partial4<br />Initialized Partial 5<br />Initialization looks <br />promising…<br />Template + Initialized partial 5<br />Template + Initialized partial 4<br />*Blue/Orange-nuclei<br /> Green/Yellow-gsc expression pattern<br />
    53. 53. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Method Evaluation<br />Four different methods implemented<br />Evaluation only by visual inspection of the results<br />Optimization algorithms not comparable unless running with optimized parameters<br />Lack of golden standard<br />Point-to-point correspondence does not exist (different embryos)<br />
    54. 54. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Method Evaluation<br />*After 100 iterations<br /><ul><li>Monomodal case, intensities are linearly related (C.C. ideal)
    55. 55. Global optimization algorithm is still computing (D.E. not suitable)
    56. 56. Initialization sufficient (Gradient Descent is “myopic”)</li></li></ul><li>Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Atlas Construction<br />Transformation parameters taken from the registration method with the most coherent performance and successful results <br />Correlation Coefficient optimized by the Gradient Descent algorithm<br />Initialization parameters (initialization) and transformation parameters (registration) applied on the third channel of three datasets<br />chd’s expression<br />snail's expression<br />Original view<br />spt’s expression<br />gsc’s expression<br />Original view<br />Original view<br />After mapping<br />After mapping<br />After mapping<br />gsc - spt<br />gsc -chd<br />gsc - snail<br />Partial view 5<br />Volume rendering of <br />gsc , third channel, registered gsc and transformed third channel<br />Partial view 3<br />Volume rendering of <br />gsc , third channel, registered gsc and transformed third channel<br />Partial view 4<br />Volume rendering of <br />gsc , third channel, registered gsc and transformed third channel<br />
    57. 57. Conclusions & Future work<br />
    58. 58. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Summary - Conclusions<br />Goal achieved<br />Designed and implemented an image processing framework able to map different gene expression patterns on a common template (for a given developmental stage)<br />Key points<br />Addition: Combine information from two channels<br />Initialization: Solves the problem of capture range for optimization<br />Registration Method: Correlation Coefficient + Gradient Descent<br />
    59. 59. Outline<br />Introduction<br />Motivation<br />Problem<br />Goal<br />ImageRegistration<br />Transformation<br />Interpolation<br />Similarity Measure<br />Optimization<br />Resampling<br />Design & Implementation<br />Concept<br />Overview<br />Registration Pipeline<br />Atlas Construction Pipeline<br />Tools<br />Implementation<br />Results & Evaluation<br />Comparison of Registration Methods<br />Atlas <br />Conclusions & Future Work<br />Conclusions-Future work<br />Advantages<br />Modularity<br />Configurability<br />Semi-automated<br />Future work<br />More datasets -> more gene expression patterns<br />Other developmental stages<br />Validated with known gene regulatory networks<br />
    60. 60. Thanks to…<br />Biomedical Image Technologies Laboratory (BIT)<br />Technical School Of Telecommunications Engineering (ETSIT)<br />Technical University of Madrid (UPM)<br />
    61. 61. Thank you for your attention<br />

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