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http://www.brain-map.org. A Big Thanks. Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University. Dr. Luis Ibanez Open Source Proponent, ITK Kitware Inc. Supplemental Material. Allen Mouse Brain Atlas.
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A Big Thanks Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Dr. Luis Ibanez Open Source Proponent, ITK Kitware Inc.
Allen Mouse Brain Atlas • Genome-wide atlas of gene expression throughout the mouse brain (N=1,2 or a few mice/gene) • 56 day-old (young adult) C57BL/6J mice • High-throughput experiments using in situ hybridization • Pipeline - sectioning, ISH, digital microscopy, image analysis, atlas registration
The Process Construction and representation of the Anatomic Gene Expression Atlas (AGEA).
Nissl-Stained Atlas – Ground Truth (a) Level 53 coronal plate (bregma 0.145 mm) from the The Allen Reference Atlas (ARA) delineating 2D anatomic boundaries of a Nissl-stained mouse brain section.
Bregma – Neurological Context Level 53 coronal plate (bregma 0.145 mm) bregma located at the intersection of the coronal and sagittal sutures. http://en.wikipedia.org/wiki/Bregma
Nissl Motor nerve cell from ventral horn of medulla spinalis of rabbit. The angular ande spindle-shaped Nissl bodies are well shown • Nissl stains the cell body esp. endoplasmic reticulum. • Basic dyes (e.g. aniline, thionine, or cresyl violet) to stain negatively charged RNA blue, • Nisslsubstance (rough endoplasmic reticulum) appears dark blue from ribosomal RNA • DNA stains a similar color Nissl-stained histological section through the rodent hippocampus showing various classes of cells (neurons and glia). http://en.wikipedia.org/wiki/File:NisslHippo2.jpg
Atlas Assembly (b) 3D assembly of high-level ARA structures formed by 3D reconstruction of the Nissl sections. The 3D ARA space is partitioned into 200-mm^3 voxels forming the smallest spatial unit for analysis. • New annotated anatomical reference atlas (Hong-Wei Dong, 2007) • 528 coronal Nissl sections: unfixed, frozen mouse brain (25μm thick) • 132 sections, with 100μm spacing, annotated over1000 brain • All image data are mapped to common coordinate space • Waxholm - http://en.wikipedia.org/wiki/Waxholm_space
Creating Geometry from Images H+E Slides Alignment Aperio Placenta Visualization/Surface Extraction Segmentation
Virtual Cellular Reconstructions Before using cellular segmentation Using cellular segmentations
Plane-by-Plane Reconstruction Mammary duct segmentation Visualization: N-point function feature space
Sub-Sampling by Half New Spacing S’x New Spacing S’y New Origin (O’x,O’y) Origin (Ox,Oy)
Resampling in ITK Origin Spacing Region Size Resample Filter Region Start Transform Interpolator
Formulation I2(x,y)=g(I1(f(x,y))f() – spatial transformationg() – intensity transformation • Assume correspondences are known • Find such f() and g() such that the images are best matched
General Formulation The general formulation for registration with regularization is: where is the Error term is the regularization parameter is the penalty term
Registration FixedImage Metric Optimizer Interpolator MovingImage Transform
Image Metrics • Mean Squares • Normalized Correlation • Mean Reciprocal Square Difference • Mutual Information- Viola-Wells- Mattes- Histogram based- Histogram normalized
Plotting the Metric Mean Squared Differences Transform Parametric Space
Plotting the Metric Mean Squared Differences Transform Parametric Space
Plotting the Metric Mean Squared Differences – A PROBLEM Transform Parametric Space
Registration FixedImage Metric Optimizer Interpolator MovingImage Transform
Transforms • Translation • Scaling • Rotation • Rigid3D • Rigid2D • Affine • BSplines • Splines: TPS, EBS, VS
Rigid Transformation • Rotation(R) • Translation(t) • Similarity(scale)
Registration FixedImage Metric Optimizer Interpolator MovingImage Transform
Interpolators • Nearest Neighbor • Linear • BSpline
Optimizers • Gradient Descent • Regular Step Gradient Descent • Conjugate Gradient • Levenberg-Marquardt • One plus One Evolutionary Algorithm
G( x , y ) = ∆ f( x , y ) Gradient Descent Optimizer f( x , y ) S = L ∙ G( x , y )
G( x , y ) = ∆ f( x , y ) Gradient Descent Optimizer f( x , y ) L too large S = L ∙ G( x , y )
G( x , y ) = ∆ f( x , y ) Gradient Descent Optimizer f( x , y ) L too small S = L ∙ G( x , y )
Components Registration in ITK MultiResolutionRegistrationFramework ImageRegistrationFramework PDEBasedRegistration FEMBasedRegistration
Allen Reference Atlas • 3D Nissl volume comes from rigid reconstruction • Each section reoriented to match adjacent images as closely as possible • A 1.5T low resolution 3D average MRI volume used to ensure reconstruction is realistic • Reoriented Nissl section down-sampled, converted to grayscale • Isotropic 25μm grayscale volume.
Anatomy • 208 large structures and structural groupings extracted • Projected & smoothed onto 3D atlas volume to for structural annotation • Additional decomposition of cortex into an intersection of 202 regions and areas