Vessel Metrics: A python based software tool for automated analysis of vascular structure in confocal imaging
Bibliographic record
Abstract
Abstract Images contain a wealth of information that is often under analyzed in biological studies. Developmental models of vascular disease are a powerful way to quantify developmentally regulated vessel phenotypes to identify the roots of the disease process. We present vessel Metrics, a software tool specifically designed to analyze developmental vascular microscopy images that will expedite the analysis of vascular images and provide consistency between research groups. We developed a segmentation algorithm that robustly quantifies different image types, developmental stages, organisms, and disease models at a similar accuracy level to a human observer. We validate the algorithm on confocal, lightsheet, and two photon microscopy data in zebrafish. The tool accurately segments data taken by multiple scientists on varying microscopes. We validate vascular parameters such as vessel density, network length, and diameter, across developmental stages, genetic mutations, and drug treatments, and show a favorable comparison to other freely available software tools. Vessel Metrics reduces the time to analyze experimental results, improves repeatability within and between institutions, and expands the percentage of a given vascular network analyzable in experiments. Summary statement Vessel Metrics is an automated software tool designed to standardize and streamline the analysis of vascular microscopy images.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".