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Creating an Automated Blood Vessel Diameter Tracking Tool. Peter McLachlan Department of Medical Biophysics The University of Western Ontario Supervisor: Dr. Graham Fraser Co-supervisor: Dr. Dwayne Jackson. Introduction. Blood flow is modulated to meet metabolic demands

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slide1

Creating an Automated Blood Vessel Diameter Tracking Tool

Peter McLachlan

Department of Medical Biophysics

The University of Western Ontario

Supervisor: Dr. Graham Fraser

Co-supervisor: Dr. Dwayne Jackson

slide2

Introduction

  • Blood flow is modulated to meet metabolic demands
  • Blood flow, Q, described by fluid flow equations
    • Ohm’s law:
    • Poiseuille’s law:
      • Small change in radius  large change in flow
    • Need to measure vessel diameters
slide3

Motivation

  • Currently, diameters are measured in ImageJ

In vivo video stills

slide4

Motivation

  • A graduate student may perform:
    • 1 experiments / week
    • ~10 000 images / experiment (conservatively!)
    • ~5 seconds per measurement with ImageJ
    • = 700 person hours per year
      • Very time consuming!
      • This is twice the time to run the experiment
  • This process needs to be automated
slide5

Current Attempts

  • Sarelius:
    • purely horizontal vessels and sub-regions
    • successful but limited to pre-aligned vessels
  • Our goal:
    • vessels at any orientation
    • more general sub-regions:
      • can vary the position wrt the input points
slide6

Project Objectives

  • Image Registration
    • In vivo microscopic sequences of blood flow
    • Minimize motion in sequence frames
  • Vessel Diameter Measurement
    • Automated over video sequence
slide7

Quick Overview

  • Outline of programming tasks:

In vivo microvessel video

Image Registration

User inputs initial diameter seed points

Track input points over sequence

Output diameter measurements

slide8

Why Image Registration?

  • Consecutive frames experience tissue motion
    • Breathing
    • Response to experimental intervention
slide10

Image Registration

  • Measure shift of a frame wrt reference frame
    • measure similarity to a reference frame
  • 2D normalized cross-correlation: similarity of frames
    • outputs correlation amplitude as function of x,y
slide11

Methods

  • Correlation amplitude plotted versus position (x,y)
  • Best overlap: at the position of maximum similarity
  • Calculate offset of frame to reference from this
  • Repeat for every frame
slide13

Image Registration

  • Correlation Amplitude: how good is the match
slide14

Project Objectives

  • Image Registration
    • Minimize tissue motion in video
  • Vessel Diameter Measurement
    • Automate over video sequence
slide15

Diameter Measurement

  • User inputs two seed points in first image
  • Diameter is distance between two points
slide16

Diameter Measurement

  • Program creates sub-regions around seed points
  • Compute similarity of current frame sub-region to reference frame sub-region
slide17

Diameter Measurement

  • Peak cross-correlation amplitude how far the regions have moved
  • Shift seed points by offset and re-calculate diameter

d

slide18

Feature Tracking

First frame with seed pointsfrom user

Go to next frame

No

Final frame?

Create sub-regions based on input points from previous frame

Calculate new points (and diameter) from peak cross-correlation offset

Yes

End

slide19

Model Validation

  • Obtained expert manual diameter measurements
    • The gold standard
  • Compare these to diameters generated by the program with the same initial seed points
slide20

Results

  • Expert manual measurement
slide21

Results

  • Expert manual measurement
slide22

Results

  • Expert manual measurement
slide23

Conclusions

  • Successfully stabilized tissue motion in sequences
  • Software is capable of making automated diameter measurements
  • Resulting diameter measurements are on average within1.5 microns of the gold standard
  • Some post-hoc analysis and selection of results may be necessary (to identify periods of poor measurements)
slide24

Future Work

  • Test software on other sequences and imaging techniques
  • Test with other similarity metrics
  • Expand functionality to measure multiple vessels and ROIs along a single vessel
slide25

Acknowledgements

  • Dr. Graham Fraser
  • Dr. Dwayne Jackson
  • Nicole Novielli
slide26

References

  • Lee, J., Jirapatnakul, A., Reeves, A., Crowe, W., Sarelius, I. Vessel Diameter Measurement from Intravital Microscopy Annals of Biomedical Engineering, Vol. 37, No. 5, May 2009 (2009) pp. 913–926
  • Brown, L. G. A survey of image registration techniques. ACM Comput. Surv. 24(4):325–376, 1992.
  • J. P. Lewis. Fast Normalized Cross-Correlation. Industrial Light & Magic
slide27

Future Work

  • Optimize correlation amplitude
    • Test software on other sequences and imaging techniques
    • Test various size and position of sub-regions
    • Test with other similarity metrics
  • Expand functionality to measure multiple vessels and ROIs along a single vessel
slide28

Results

  • Non-expert Measurement
slide29

Tracking

  • Video of tracked diameters:
slide30

Diameter Measurement

  • Automated method

J. Lee et al., Annals of Biomedical Eng., V. 37. No. 5:913–926, 2009

slide31

Image Registration

  • Pick sub-regions in an image
  • Compare relative positions with respect to reference
  • Best position: where regions have the highest similarity