Rapid and reliable image analysis pipeline for semi-automated quantification of CNS cell types in MATLAB
Bibliographic record
Abstract
BACKGROUND: Studying CNS cell responses is essential for understanding disease, injury, and developing effective therapies. While immunofluorescence and transgenic reporter models allow for specific labeling, automated quantification remains difficult due to tissue heterogeneity. Consequently, most analyses are conducted manually, introducing user bias and limiting reproducibility. NEW METHOD: We developed a MATLAB-based semi-automated workflow for quantifying immunofluorescence-stained CNS cells, focusing on nuclear signal detection. The pipeline uses DAPI masking and the imfindcircles function to detect round nuclei, requiring minimal user input. RESULTS: The pipeline enabled robust quantification of CNS-resident cells. Automated analyses of brain and spinal cord tissue sections closely resembled manual quantification, with minimal error. In a mouse model of contusion spinal cord injury, it revealed a rostro-caudal decline in myelinating oligodendrocytes from the lesion epicenter, confirming the method's accuracy and sensitivity in detecting injury-induced cellular changes. COMPARISON WITH EXISTING METHODS: Unlike many commonly used quantification-based software, this novel pipeline does not perform full image segmentation. Instead, it uses nuclear morphology to detect round shapes. Moreover, the pipeline has been specifically designed and optimized for the quantification of CNS cells, whose heterogeneity and cytoarchitecture pose specific challenges to existing methods that are more generalized. CONCLUSIONS: This study presents an alternative to classical segmentation models by offering a reproducible quantification of CNS-resident cells using nuclear morphology. Its simplicity, minimal input requirements, reduced time for semi-automated quantification, and specificity for CNS tissues make it a valuable tool for studying cellular responses in both healthy and pathological contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".