2006 Mouse AHM

Mapping 2D slices to 3D atlases - Application of the Digital Atlas

Erh-Fang Lee

Laboratory of NeuroImage

UCLA

Ultimate Goal of Mouse Atlas Project

• Establish a framework and working toolset that integrates anatomical and gene expression data into a centralized and easily accessible resource.

Purpose of 2D-to-3D registration

• Map the atlas information to the input image

• Find the standard space to house the input data

Real Example : The Bmp6 expression data set of P7 from GENSAT database

Find an approximateplane from the atlas

Derive a sub-atlas which includes ROI only

Workflow for 2D Data Standardization and Incorporation

2D sub-atlas labels

2D input image

Atlas-basedRegistration/Segmentation

Data Federation

Ideal Implementation for Template Retrieving

• Modality independent.• 2D orientation independent and tolerant to the

variation in scale to certain range.• Minimal manual data preprocessing.• Quick search and comparison.• Region of interest is registered.

Proposed Approach

• Find the searching range which generates the 2D sub-atlas containing the structure of interest.

• Digitally section over the sub-volume and generate 2D atlas planes.

Generate the 2D atlas planes by digitally sectioning over the brain

Proposed Approach

• Find the searching range which generates the sub-atlas containing all structures of interest.

• Digitally section over the sub-volume and generate 2D atlas planes.

• Find the plane which is most similar to the input image from the set of 2D atlases.

Searching approach based on brain shape comparison• Modified from “Ryutarou Ohbuchi, Tomo Otagiri,

Masatoshi Ibato, Tsuyoshi Takei: Shape-Similarity Search of Three-Dimensional Models Using Parameterized Statistics. 2002”

• The shape of the brain is modeled as features vector which is parameterized statistics of the contour along the two principle axes of inertia.

• The shape distance between two brains is the Euclidean distance between the feature vectors formed from these parameters.

Parameterized statistics for shape comparison

1. Parse the brain contours from masked data using a 4-neighbor edge detector.

2. Align each comparison set using the center of mass and the principle axes of inertia.

3. The brain is subdivided into slabs along the two principle axes of inertia. The brain shape is modeled as the combination of feature vectors composing of

1. The moment of inertia

2. The average distance of a contour point from the axis

3. The variance of distance of a contour point from the axis

Shape model and the definition of distance

• Shape model :

• Shape distance :

Parameterized statistics for shape comparison– A bunny model example of parameterized statistics along

one axis

Ryutarou Ohbuchi, Tomo Otagiri, Masatoshi Ibato, Tsuyoshi Takei: Shape-Similarity Search of Three-Dimensional Models Using Parameterized Statistics. 2002

Modification from Ohbuchi’s Model

• Includes options for the shape distance computation– Weighted the statistics with axis ratio – Weighted the statistics with the product of axes

Implementation

The Bmp6 expression data set of E16 from GENSAT database

Result of Serial Insertion of the GENSAT images

The Bmp6 expression data set of P7 from GENSAT database

Map the P0 Atlas to GENSAT data of P7

Observation from some experiment• Better retrieving for planes from sagittal and horizontal

sections• Using less weight for variation term in feature vectors

improves the retrieving for distorted images.• Manually adjustment, if necessary, is usually within

few pixels and 10 degrees to obtain a better approximation.

• Less robust for planes from more distal part of the brain

• Sensitive to the “integrity” of the brain contours

Possible methods to improve the accuracy of retrieving

• More anatomical restriction to narrow the searching range.

• Includes boundaries of some anatomical structures into the shape model.– Additional delineation on input image would be

required

Conclusion

• Alleviate the effort on atlas navigation for users who have mild knowledge in brain anatomy.

• Potential Usage :– Derive a 2D reference space(s) for further

nonlinear registration. – Spatially query the atlas information.– Provide a template for atlas-based segmentation

and registration.

Find an approximateplane from the atlas

Derive a sub-atlas which includes ROI only

Workflow for 2D Data Standardization and Incorporation

2D sub-atlas labels

2D input image

Atlas-basedRegistration/Segmentation

Data Federation