  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.
