||Mouse Embryo Visual Project
To understand the mechanisms of human development, embryologists study
both normal development and development that leads to the birth defects.
One way to gain further understanding of the development of an embryo is
through 3-D reconstruction from serial sections taken from an optical microscope.
The Mouse Embryo Visual Project (MEVP) is intended to develop a semi-automatic
system utilizing the concepts of computer graphics and 3D visualization
and at the same time allow to explore the anatomy of a mouse embryo. The
modern segmentation method based on the modification of 'snakes' will be
used to speed up the process.
CONTOUR-BASED 3D RECONSTRUCTION
The contour-based reconstruction, begins with tracking of contours that
outline structures at a series of slices. The traced contours are stored
in a database, and various methods can be use to render the contoured object.
This reconstruction method is useful for observing the shape of the object
of data compression because the 3-D volume of a structure is represented
simply by a surface created by forming tiles between the adjacent contour
lines. Contour based 3-D reconstruction is widely used with light microscope
sections, and with conventional thin electron microscope sections. The
contour-based reconstruction is also the method of choice for reconstructions
where there are significant discontinuities between structures on adjacent
sections. It is also useful for morphological measurements. Both commercial
and otherwise available software for 3D reconstruction of MRI, CT, confocal
and serial-secion data for medical sciences is listed at http://biocomp.arc.nasa.gov/3dreconstruction/software/.
See 3D Reconstruction
homepage for more information.
The alternative to contour-based reconstruction is volume
rendering. There are several software packages
available for volume rendering. Images obtained from volume rendering contain
the information inside the object, and they are useful for over viewing the object. However, it is difficult to observe the precise shape of the
object from images, due to the difficulty of determination which point
in volume is actually contributing to the final projection.
THE TECHNIQUE FOR ACQUIRING THE MOUSE EMBRYO DATA
We are going to observe a mouse embryo including its inner structures at
development stage approximately 10.5 days old.
Photo of a mouse embryo before processing.
The size of the embryo in so early development stage is very small, i.e.,
about 4.5 mm in height. A normal way to observe the inner structures of
a microscopically small spacemen is to use cutting sections from the spacemen.
The mouse embryo, in our case, was further sectioned with an ultramicrotome
at an average thickness of section about 7 micrometers. This gives us 636
cross-sections that are further processed.
If alignment of the sections is exact, close spacing will show accurate
detail in the 3-D reconstruction. However, sections cut from biological
spacemens are often slightly distorted, particularly if using ultra-thin
sections. Sections cannot then be completely aligned across their full
width and this may result in spurious detail and a false impression of
the morphology. It may be possible to correct a distortion by manipulation
of the images or the data. To solve this problem we explore the image registration
techniques over serial cross-sections.
Making a microscope preparation from mouse embryo is a very complex problem,
which consist of following steps:
Each cross-section is mounted on glass and then scanned at magnification
of 74 times by an optical microscope, to which is attached a photo camera.
Images taken from the photo camera are stored in high-resolution compact
discs with after conversion into digital images. The resolution of digital
images was selected to be 720x580. We have experimented with a CCD video
camera connected to graphics work-station and attached to the microscope.
The resolution of the CCD video camera is not so high. For comparable resolution
with a photo camera, we need to use a HDTV (high vision) video camera.
Fixation: killing cells and hardening a tissue with acid, alcohol,
heavy metal, aldehyde etc.
Dehydration: substituting water in the specimen with alcohol with
graded series of ethanol.
Clarification: substituting ethanol with toluol.
Infiltration of melted paraffin into tissue.
Embedding into paraffin block.
Sectioning (5-10 micrometers thick) using a microtome.
Putting sections on a slide glass, flattening them by gentle heating.
Deparaffinization: removal of paraffin in toluol.
Hydration: substituting toluol with water using reversely graded
Staining: Using a Hematoxyline-Eosine method the double staining
nuclei of cells are stained blue by hematoxyline, and some other structures
are stained red by eosine.
Mounting into microscopic preparation.
Scanned mouse embryo cross-section. It shows embryo's head and brain.
Registration refers to the alignment of data from the same or different
sensors. Alignment of sections from serial microscopy is required to compensate
for combination of the two, misregistration and physical changes over the
space. Sections from serial microscopy are not only translated and/or rotated.
Often they are deformed due to the heating required in the preparation
of the tissue for microscope slices. Thus, serial microscopy requires a
combination of a linear transformation to bring the sections into approximate
alignment, followed by a nonlinear transformation to account for distortions
in the tissue preparation process.
To reconstruct the three-dimensional structure of the mouse embryo from
the set of cross-sections, all sections must share a common reference coordinate
system. One approach for finding the approximation of the reference coordinate
system is as follows: The entire mouse embryo is embedded into a paraffin
block to make the sectioning process easier. Four marks can be made in
the paraffin with four shots of laser and consequently filled with gelatin.
The gelatin marks on each section determine the reference coordinate system
that can be approximated using the image registration techniques. However,
because important data images were already taken without marks, no reference
marks were adopted. Therefore, the image registration process is necessary
to find a good approximation of the reference coordinate system.
In the majority of reconstructions the images can be satisfactory aligned
by eye. It is possible to establish the best fit for the three variables,
x- and y- shift and rotation. An alternative of manual technique for aligning
two images, is that the user translates and rotates one of the images while
the other is held stationary. This method uses color combination instead
of motion used in. The two images are colored using two distinct colors,
and images are added to form a third color. The best alignment is easy
to recognize because it maximizes the area of the third color.
Vectors and landmarks shown in above images determine the translation and
rotation of images when two vectors align.
The best registration.
The series of sections is registered by repeating the registration of
image pairs from top to bottom or vice versa. We should remember that small
error of alignment can accumulate a significant shift or rotation over
many sections. It is therefore necessary to register the actual section
also to the sections that are far away.
CONTOUR EXTRACTION FROM IMAGE DATA
The large morphometric variability in biomedical organs requires an accurate fitting
method for a pregenerated contour model. We are seeking the outline contours of
a different shapes visible in the cross-sections. We proposed and implemented
a physically based approach to the fitting of two-dimensional contours using texture
feature vectors. This method is an extension of well known 'snake' model allowing
even automatic contour splitting into several parts. (Images
related to the snake model.)
Unfortunately, the necessary numerical iteration process requires a
good approximation of an initial step solution. Generally, the approximation
is carried out interactively tracking the contour using a mouse locator
near the edges within the image cross-sections. This initialization need
not be performed for each contour and all sections. Setting the initial
position near the edges of a target object only for a few selected cross-sections
is sufficient. Results from one section are automatically used in the next
cross-section as initial position. Continuing this process for all cross-sections
we will finally have a stack of contours without knowing how are they related.
This is an example of what is the result in this step:
Stack of extracted contours.
SURFACE FORMATION FROM CONTOUR DATA
First, the correspondence problem must be solved between multiple contours
in the adjoined sections. Last, the triangulation between each pair of
contours must be performed. The result of the triangulation is a surface
enveloping the stack of extracted contours called "geometric" model. Such
a geometric model is built for each structure of interest.
The next step is to present the models in a way that allows people to understand
the shape and relationship between models. This is done by visualization techniques.
Now, you can see several images showing different
parts of a mouse embryo.
Reconstructed mouse embryo.
© Roman Durikovic 1998