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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".