Automated methods for neuron segmentation and analysis of electron microscope images
Bibliographic record
Abstract
To study changes in neuron size, number and distribution over a wide range of animal sizes, it is necessary for us to identify the axons and myelin of each neuron in electron microscope images of nerve cross-sections. Current methods commonly in use involve manually labeling each axon, which is extremely time-consuming as a single nerve contains thousands of axons. In order to make this process more efficient, we developed a computer-assisted neuron segmentation and analysis method. First we acquired a set of sub-images with identical size and resolution using a scanning electron microscope. We then developed an algorithm which used cross-correlation to stitch the sub-images into large images containing whole neuron clusters for segmentation. We developed a second algorithm to pre-process the stitched image, then segment and individually label axons using combined morphological operations. The myelin of each neuron was also segmented using a region growing algorithm with the geometric centers of axons as seeds. The final output of our algorithm is a histogram of axon and myelin sizes. We used this method to analyze nerves from different animal species including elephant, rat and shrew [1]. The typical processing time for a 4~6 million-pixel image on a PC (1.66GHz Pentium M Processor, 1G RAM) was approximately 5 minutes. The mislabel rate (percentage of false detections plus failed detections) is currently under 10% and improving. The method was proven to be well-suited for studying the effect of animal size on axon size and number.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".