OSLO: Automatic Cell Counting and Segmentation for Oligodendrocyte Progenitor Cells

Abstract

Reliable cell counting and segmentation of oligodendrocyte progenitor cells (OPCs) are critical image analysis steps that could potentially unlock mysteries regarding OPC function during pathology. We propose a saliency-based method to detect OPCs and use a marker-controlled watershed algorithm to segment the OPCs. This method first implements frequency-tuned saliency detection on separate channels to obtain regions of cell candidates. Final detection results and internal markers can be computed by combining information from separate saliency maps. An optimal saliency level for OPCs (OSLO) is highlighted in this work. Here, watershed segmentation is performed efficiently with effective internal markers. Experiments show that our method outperforms existing methods in terms of accuracy.

v1: Submitted to ICIP 2018; v2: Accepted to be published in 2018 IEEE International Conference on Image Processing, Oct 7-10, 2018, Athens, Greece. IEEE Copyright notice added. Minor changes for camera-ready version