Microglia Cell Segmentation Using a Hand-crafted Method Capable of Handling High Noise Levels in Image Data
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Microglia are the resident immune cells in the brain and spinal cord, playing a crucial role in various pathological processes. Accurate segmentation of microglia is the first and most critical step in the analysis of their morphology, which serves as a labelfree, primary indicator of microglial phenotype. In this work, we present a fully automated microglia segmentation method that is capable of reliably detecting and segmenting microglia from surrounding tissue, even under challenging conditions with substantial tissue-caused background noise in the image data. Our method incorporates several novel approaches, including a highly effective way to remove background noise while preserving microglial structures and an approach for filtering out microglial structures without an associated cell nucleus. We compared our microglia cell segmentation method with three well-known segmentation approaches reported in previous work on microglial morphology. The methods were applied to 20 fluorescence microscopy images of the spinal cord containing hundreds of microglia, for which a manually segmented ground truth segmentation has been obtained. We show that our proposed method clearly outperforms the previous methods.
Description
CCS Concepts: Computing methodologies->Machine learning; Applied computing->Computational biology
@inproceedings{10.2312:vcbm.20251251,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Garrison, Laura and Krueger, Robert},
title = {{Microglia Cell Segmentation Using a Hand-crafted Method Capable of Handling High Noise Levels in Image Data}},
author = {Wimmer, Georg and Khan, Ibrahim and Bieler, Lara and Benedetti, Bruno and Couillard-Despres, Sebastien and Uhl, Andreas},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-276-9},
DOI = {10.2312/vcbm.20251251}
}