Random-Forest-Based Automated Cell Detection in Knife-Edge Scanning Microscope Rat Nissl Data Conference Paper uri icon


  • © 2015 IEEE. Rapid advances in high-resolution, high-throughput 3D microscopy techniques in the past decade have opened up new avenues for brain research. One such technique developed in our lab is called the Knife-Edge Scanning Microscopy (KESM). The basic principle of KESM is to line-scan image while simultaneously sectioning thin tissue blocks using a diamond microtome. We have successfully sectioned and imaged whole mouse brains and portions of a rat brain processed with different stains to investigate the microstructures within. In this paper, we will present a fully automated soma (cell body) detection method based on random forests, working on Nissl-stained rat brain specimen. The method enables fast and accurate cell counting and density measurement in different brain regions.

altmetric score

  • 3

author list (cited authors)

  • Das, S. L., Keyser, J., & Choe, Y.

citation count

  • 2

publication date

  • July 2015