OSLO: Automatic Cell Counting and Segmentation for Oligodendrocyte Progenitor Cells

TL;DR

This paper introduces OSLO, an automatic method combining saliency detection and watershed segmentation to accurately count and segment oligodendrocyte progenitor cells in microscopy images, aiding neurological research.

Contribution

The paper presents a novel saliency-based approach with an optimal saliency level for improved OPC detection and segmentation, outperforming existing methods.

Findings

Outperforms existing methods in accuracy

Efficient segmentation with internal markers

Effective detection of OPCs in microscopy images

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.