Automated image analysis system for detecting boundaries of live prostate cancer cells

Image analysis provides a powerful tool for quantifying cell motility and has been used to correlate motility with metastatic potential in an animal model of prostate cancer. However, widespread use of this image analysis method has been limited because earlier methods of quantitative analysis required time-intensive and subjective manual tracing of cell contours. In this report, we describe a fully automated image segmentation algorithm for detection and morphometric description of prostatic cells. The segmentation system was tested on prostate cell images generated from Hoffman modulation contrast microscopy (47 cells at 64 time points = 3,008 images) and differential interference contrast microscopy (29 cells at 64 times points plus 1 cell at 62 time points = 1,918 images). Morphometric measurements were derived from computer-determined cell boundaries and compared with the same measurements derived from manually traced cell boundaries. Final correlation coefficients for area and perimeter measurements for Hoffman and differential interference contrast microscopy were (0.76, 0.62) and (0.93, 0.93), respectively. Results with our differential interference contrast images demonstrate that our segmentation algorithm reliably and efficiently replaces the need for manually traced cell boundaries in addition to eliminating intraobserver variation. Our automated segmentation process will have immediate utility in our motility analysis system that relates cell motility with metastatic potential of prostate cancer.