Background
Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The
Conclusion
With great clinical, diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach.
Methods
Light microscopy images of renal tissue sections stained for VEGF were analyzed. Subsequently, machine learning classified the cells as VEGF+ and VEGF- cells.
Results
VEGF was detected and cells were counted with high sensitivity and specificity.