a powerful tool for live-time treatment
planning of coronary artery interventions by adding functionality such as
integration with a real-time 3D visualization module that will be able to
quantify (volume, area covered, and
others) the presence of calcified
regions. An example of such visualization is shown in figure 9, which is
implemented by stacking the output of
multiple 2D images.
This can help in decision making
regarding stent implantation and
preimplantation treatment, or plaque
remodeling (for example, directional
atherectomy). We also plan to add an
explanatory module to help explain
the automated classification process to
the interventional cardiologists and to
accept feedback in an active learning
environment. Finally, we will develop
an easily accessible web-based tool for
offline analysis of IVOCT images.
We expect that such a tool will be
used by entities requiring fast analysis
that can provide data useful for drug
assessment, experimental therapeutics,
and experimental medical devices.
This project was supported by Ohio
Third Frontier, and by the National
Heart, Lung, and Blood Institute
through grants NIH R21HL108263 and
1R01HL114406-01, and by the Nation-
al Center for Research Resources and
the National Center for Advancing
Translational Sciences through grant
UL1RR024989. These grants are collab-
oration between Case Western Reserve
University and University Hospitals of
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Figure 7. Example of Validation Analysis.
Top row images (from left to right): cryo-image fluorescence, IVOCT, expert annotation of IVOCT guided by registered cryo-image, results
of automated classification, and automated classification after noise cleaning. In the bottom row, image data from a different vessel segment are shown with the exception that the fluorescence image is replaced by the color cryo-image. Calcium, fibrous, and lipid are labeled
red, blue, and green respectively. Note the good correspondence between the third and fifth columns, indicating good classifications.
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