Creating an Automated Blood Vessel Diameter Tracking Tool

Peter McLachlanDepartment of Medical BiophysicsThe University of Western Ontario

Supervisor: Dr. Graham FraserCo-supervisor: Dr. Dwayne Jackson

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

• Currently, diameters are measured in ImageJ

Motivation

In vivo video stills

• 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

Motivation

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

1. Image Registration• In vivo microscopic sequences of blood flow• Minimize motion in sequence frames

2. Vessel Diameter Measurement• Automated over video sequence

Project Objectives

Quick Overview

In vivo microvessel

video

User inputs initial diameter seed

points

Image Registration

Track input points over sequence

Output diameter measurements

• Outline of programming tasks:

Why Image Registration?

• Consecutive frames experience tissue motion

• Breathing

• Response to experimental intervention

Raw Video

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

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

Registered Video

Image Registration• Correlation Amplitude: how good is the match

1. Image Registration• Minimize tissue motion in video

2. Vessel Diameter Measurement• Automate over video sequence

Project Objectives

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

𝑑2=(𝑥¿¿2−𝑥1)2+(𝑦2− 𝑦1)

2 ¿

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

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

𝑑2=(𝑥¿¿2−𝑥1)2+(𝑦2− 𝑦1)

2 ¿

d

Feature TrackingFirst frame with

seed pointsfrom user

Create sub-regions based on input

points from previous frame

Final frame?

Go to next frame

Calculate new points (and

diameter) from peak cross-

correlation offset

End

Yes

No

Model Validation• Obtained expert manual diameter measurements

• The gold standard

• Compare these to diameters generated by the program with the same initial seed points

Results• Expert manual measurement

Results• Expert manual measurement

Results• Expert manual measurement

Conclusions• Successfully stabilized tissue motion in sequences

• Software is capable of making automated diameter measurements

• Resulting diameter measurements are on average within 1.5 microns of the gold standard

• Some post-hoc analysis and selection of results may be necessary (to identify periods of poor measurements)

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

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

Acknowledgements

• 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

References

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

Results• Non-expert Measurement

Tracking• Video of tracked diameters:

•

Diameter Measurement• Automated method

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

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