Microglia Cell Segmentation Using a Hand-crafted Method Capable of Handling High Noise Levels in Image Data

Simple item page
dc.contributor.authorWimmer, Georgen_US
dc.contributor.authorKhan, Ibrahimen_US
dc.contributor.authorBieler, Laraen_US
dc.contributor.authorBenedetti, Brunoen_US
dc.contributor.authorCouillard-Despres, Sebastienen_US
dc.contributor.authorUhl, Andreasen_US
dc.contributor.editorGarrison, Lauraen_US
dc.contributor.editorKrueger, Roberten_US
dc.date.accessioned2025-09-24T09:11:59Z
dc.date.available2025-09-24T09:11:59Z
dc.date.issued2025
dc.description.abstractMicroglia 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.en_US
dc.description.sectionheadersSession 3
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20251251
dc.identifier.isbn978-3-03868-276-9
dc.identifier.issn2070-5786
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/vcbm.20251251
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vcbm20251251
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies->Machine learning; Applied computing->Computational biology
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectApplied computing
dc.subjectComputational biology
dc.titleMicroglia Cell Segmentation Using a Hand-crafted Method Capable of Handling High Noise Levels in Image Dataen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
vcbm20251251.pdf
Size:
39 MB
Format:
Adobe Portable Document Format
Download
Collections
VCBM 2025: Eurographics Workshop on Visual Computing for Biology and Medicine