Multistage histopathological image segmentation of Iba1-stained murine microglias in a focal ischemia model:
Nektarios A Valous1, Bernd Lahrmann, Wei Zhou
1Hamamatsu Tissue Imaging and Analysis Center, Bioquant, Department of Medical Oncology, National Center for Tumour Diseases, Heidelberg University, Im Neuenheimer Feld 267 (BQ 0010), D-69120 Heidelberg, Germany.
Abstract:
A multistage workflow was developed for segmenting and counting murine microglias from histopathological brightfield images, in a permanent focal cerebral ischemia model. Automated counts are useful, since for the assessment of inflammatory mechanisms in ischemic stroke there is a need to quantify the brain's responses to post-ischemia, which primarily is the rapid activation of microglial cells. Permanent middle cerebral artery occlusion was induced in murine brain tissue samples. Positive cells were quantified by immunohistochemistry for the ionized calcium-binding adaptor molecule-1 (Iba1) as the microglia marker. Microglia cells were segmented in seven sequential steps: (i) contrast boosting using quaternion operations, (ii) intensity outlier normalization, (iii) nonlocal total variation denoising, (iv) histogram specification and contrast stretching, (v) homomorphic filtering, (vi) global thresholding, and (vii) morphological filtering. Workflow counts were validated on an image subset, with ground-truth data acquired from manual counts conducted by a neuropathologist. Automated workflow matched ground-truth counts pretty well; 80-90% accuracy was achieved, as regards to time after pMCAO and correspondence to ischemic/non-ischemic tissue.
Insights
This study presents an automated workflow for counting microglia in brain images after stroke. The method accurately quantifies these immune cells, aiding stroke research.
Area of Science:
- Neuroscience
- Immunology
- Computational Pathology
Background:
- Microglial cells are key players in the brain's inflammatory response to ischemic stroke.
- Quantifying microglial activation is crucial for understanding stroke pathophysiology and developing treatments.
- Manual counting of microglia in histopathological images is time-consuming and subjective.
Purpose of the Study:
- To develop and validate an automated multistage workflow for segmenting and counting microglia (ionized calcium-binding adaptor molecule-1 positive cells) in murine brain tissue images.
- To assess the accuracy of the automated workflow compared to manual counts by a neuropathologist.
Main Methods:
- A permanent middle cerebral artery occlusion (pMCAO) model was used in murine brain tissue.
- Immunohistochemistry for ionized calcium-binding adaptor molecule-1 (Iba1) was performed to identify microglia.
- A seven-step image processing workflow was applied: contrast boosting, intensity normalization, denoising, histogram specification, homomorphic filtering, global thresholding, and morphological filtering.
Main Results:
- The automated workflow successfully segmented and counted microglia in histopathological images.
- The automated counts demonstrated high accuracy, achieving 80-90% agreement with manual ground-truth data.
- The workflow's performance was consistent across different time points post-pMCAO and in both ischemic and non-ischemic tissues.
Conclusions:
- The developed automated workflow provides a reliable and efficient method for quantifying microglia in stroke models.
- This tool can significantly aid in the assessment of inflammatory mechanisms and therapeutic interventions for ischemic stroke.
- The high accuracy of the automated counts supports its utility in preclinical stroke research.
