HP Innovation Journal Issue 10: Fall 2018 | Page 71

we apply MVG-based methods directly,
the reconstructed points no longer form
a smooth surface, but rather represent
scattered points spread out inside the cell.
In other words, we need to reconstruct
multiple structures inside the entire volume
of the cell from the same number of images
that surface reconstruction uses today.
Our solution combined Structure from
Motion (SfM) with predefined templates of
cell structures. If we have prior knowledge
about the cell, such as a few templates
for the structures to be reconstructed, it
is possible to reconstruct the entire cell
volume from a finite number of key image
features that defines each template.
We then run the reconstruction algorithm
on several video frames. Figure 3(a) shows a
sample of the original images captured by the
video camera. The reconstructed parametric
digital model is shown in Figure 3(b).
To visualize our model in the physical world,
we designed a wire structure to see internal
structures while preserving the shape of the
cell membrane and nucleus. Figure 4 shows
samples of 3D-printed cell models using the
HP MultiJet Fusion 3D printing system.
DISCUSSION
Confocal microscopy has solved the problem
of obtaining a 3D structure of a biological cell.
In this solution, the sample is moved relative
to imaging optics, while typically a pinhole in
these optics ensures that the collected light
originates from a single plane. Those planes
are then combined to produce a 3D structure
of the cell. This traditional solution has several
complications. First, cost is typically high due to
the need for precision stepping, confocal optics
and high-sensitivity cameras. Second, there is
the problem of data degradation. Light from
distant parts of the sample must pass through
sections of the sample proximal to the optics.
And the proximal parts may act as a filter or
add noise or interact with light, corrupting the
imaging of the distant parts. Third, there is data
uncertainty due to low photon flux, as data is
collected from a thin plane.
Our system costs significantly less for con-
struction than traditional confocal systems
because it does not require precision stepping,
confocal optics and high sensitivity cameras.
Additionally, the data we obtain is likely more
accurate, as there is less transmission imag-
ing—thus there is less of a filtering effect by
parts of cells proximal to the imaging optics.
To summarize, we built a noninvasive system
that applies MVG to microscopic images of
rotating cells, reconstructs 3D-cell models
automatically and transforms them into our
physical reality. In addition, as a system for cell
visualization and modeling, our solution has
broad applications, such as providing detailed
information about unique cellular processes.
We have applied our knowledge of
microfluidics and computer vision to biology
through live cell imaging. This innovation
provides a quick and effective way to model
cell structures. Also, by serving as a channel
between the micro world that cells live in and
our macro physical world, it has transformed
cell geometry data to physical reality through
3D printing.
Early Career Talents Innovation
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