1. 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

3. Currently, diameters are measured in ImageJ In vivo video stills

4. 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

5. 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

6. 1.Image Registration In vivo microscopic sequences of blood flow Minimize motion in sequence frames 2.Vessel Diameter Measurement Automated over video sequence

7. In vivo microvessel video User inputs initial diameter seed points Image Registration Track input points over sequence Output diameter measurements Outline of programming tasks:

8. Consecutive frames experience tissue motion Breathing Response to experimental intervention

11. 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

13. Correlation Amplitude: how good is the match

14. 1.Image Registration Minimize tissue motion in video 2.Vessel Diameter Measurement Automate over video sequence

15. User inputs two seed points in first image Diameter is distance between two points

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

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

18. First frame with seed points from 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

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

20. Expert manual measurement

23. 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)

24. 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

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

26. 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

27. 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

28. Non-expert Measurement

29. Video of tracked diameters:

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

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