AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis
Bibliographic record
Abstract
Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio. AxonSeg includes a simple and intuitive MATLAB-based graphical user interface (GUI) and can easily be adapted to a variety of imaging modalities. The main steps of AxonSeg consist of: (i) image pre-processing; (ii) pre-segmentation of axons over a cropped image and discriminant analysis (DA) to select the best parameters based on axon shape and intensity information; (iii) automatic axon and myelin segmentation over the full image; and (iv) atlas-based statistics to extract morphometric information. Segmentation results from standard optical microscopy (OM), SEM and coherent anti-Stokes Raman scattering (CARS) microscopy are presented, along with validation against manual segmentations. Being fully-automatic after a quick manual intervention on a cropped image, we believe AxonSeg will be useful to researchers interested in large throughput histology. AxonSeg is open source and freely available at: https://github.com/neuropoly/axonseg.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.036 |
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".