Machine learning meets classical computer vision for accurate cell identification
Bibliographic record
Abstract
Abstract High-parameter multiplex immunostaining techniques have revolutionized our ability to image healthy and diseased tissues with unprecedented depth; however, accurate cell identification and segmentation remain significant downstream challenges. Identifying individual cells with high precision is a requisite to reliably and reproducibly interpret acquired data. Here we introduce CIRCLE, a cell identification pipeline that combines classical and modern machine learning-based computer vision algorithms to address the shortcomings of current cell segmentation tools for 2D images. CIRCLE is a fully automated hybrid cell detection model, eliminating subjective investigator bias and enabling high-throughput image analysis. CIRCLE accurately distinguishes cells across diverse tissues microenvironments, resolves low-resolution structures, and can be applied to any 2D image that contains nuclei. Importantly, we quantitatively demonstrate that CIRCLE outperforms current state-of-the-art image segmentation tools using multiple accuracy measures. As high-throughput multiplex imaging grows closer toward standard practice for histology, integration of CIRCLE into analysis protocols will deliver unparalleled segmentation quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".