Automatic Segmentation and Three-dimensional Reconstruction of Fascicles in Peripheral Nerves
Bibliographic record
Abstract
Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy that do not accurately represent anatomical conditions. To better quantify and model neural anatomy across the population, we have developed an algorithm to automatically reconstruct accurate peripheral nerve models from histological cross- sections. We acquired serial median nerve cross-sections from human cadaveric samples. We developed a processing pipeline involving four steps: registration, detection, segmentation, and reconstruction. While our algorithm could not always outperform simplified anatomical models, it nonetheless provided useful anatomical information that would otherwise be lost. Fascicle detection by convolutional neural network worked particularly well and can be applied to histological images as is. The other components of the pipeline need improvements in order to build truly accurate models. This work provides a baseline from which to progress toward a fully automatic approach to constructing peripheral nerve models.
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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".