With imaging being the core approach in our lab, we spend a lot of time deciding how to extract data from pictures. A task as simple as measuring the width of a blood vessel, can actually be quite challenging to do consistently. Blood vessel diameter is the major determinant of blood flow resistance (think pinching a garden hose), and therefore even small difference in vessel diameter can lead to significant changes in blood flow. Blood vessel diameter measurements are important to get right.
So … why not just draw a line from one side of the blood vessel to the other, and measure how long the line is? Well, we asked 8 members from our lab to do just this, and the variability in outcome was substantial. People had different opinions of where the edge of the blood vessel was actually located. Also, there was no consistency for where the measurement was taken along a length of vessel.
Konnor McDowell came to our lab for a couple summers and found a solution. He developed a simple algorithm to measure vessel diameters across a multitude of regions along a vessel. It provided more consistent outcomes, less biased measurement, and far richer datasets on spatiotemporal dynamics of blood vessels in vivo. The algorithm was developed as a macro in ImageJ/Fiji, which is already widely adoption in the imaging community.
The result was an easy-to-use resource for researchers seeking to measure blood vessel diameter in imaging data. While we tested the software on two-photon imaging data from mice, we found that it also worked for clinical retina imaging data.
This work will be published in Quantitive Imaging in Medicine and Surgery, along with details of code download from Github.
Konnor is now an MD/PhD student at the Medical University of South Carolina.