1 / 31

Creating an Automated Blood Vessel Diameter Tracking Tool

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

lynn
Download Presentation

Creating an Automated Blood Vessel Diameter Tracking Tool

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

  2. 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

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

  4. 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

  5. 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

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

  7. 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

  8. Why Image Registration? • Consecutive frames experience tissue motion • Breathing • Response to experimental intervention

  9. Raw Video

  10. 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

  11. 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

  12. Registered Video

  13. Image Registration • Correlation Amplitude: how good is the match

  14. Project Objectives • Image Registration • Minimize tissue motion in video • Vessel Diameter Measurement • Automate over video sequence

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

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

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

  18. 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

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

  20. Results • Expert manual measurement

  21. Results • Expert manual measurement

  22. Results • Expert manual measurement

  23. 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)

  24. 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

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

  26. 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

  27. 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

  28. Results • Non-expert Measurement

  29. Tracking • Video of tracked diameters:

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

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

More Related