Automated tracing and change analysis of angiogenic vasculature from in vivo multiphoton confocal image time series

Abstract

Automated methods are described for tracing and analysis of changes in angiogenic vasculature imaged by a multiphoton laser-scanning confocal microscope. Utilizing chronic animal window models, time series of in vivo 3-D images were acquired on approximately the same target volume of the same specimen while undergoing angiogenic change (typically every 24 h for 7 days). Objective, precise, 3-D, rapid, and fully automated vessel morphometry was performed using an adaptive tracing algorithm that is based on a generalized irregular cylinder model of the vasculature. This algorithm was found to be not only adaptive enough for tracing angiogenic vasculature, but also very efficient in its use of computer memory, and fast, taking less than 1 min to trace a 768 x 512 x 32, 8-bit/pixel 3-D image stack on a Dell Pentium III 1-GHz computer. The automatically traced centerlines were manually validated on six image stacks and the average spatial error was measured to be 2 pixels, with an average concordance of 81% between manual and automated traces on a voxel basis. The tracing output includes geometrical statistics of traced vasculature and serves as the basis of statistical change analysis. The computer methods described here are designed to be scalable to much larger hypothesis testing studies involving quantitative measurements of tumor angiogenesis, gene expression relative to known vascular structures, and impact of drug delivery.