Deep learning‐based hybrid reconstruction algorithm for fibre instance segmentation from <scp>3D</scp> x‐ray tomographic images
Bibliographic record
Abstract
Abstract 3D x‐ray tomography is a powerful scanning technique used for generating images of complex fibre structures. A novel machine‐learning algorithm to identify and separate individual fibres using 3D images is proposed in this article. The developed four‐step hybrid 3D fibre segmentation algorithm involves deep‐learning aided semantic segmentation that slices 3D images to create 2D images for fibre extraction, elliptical contour estimation combined with the marker‐controlled watershed algorithm for separating fibres from the background area, identifying individual fibres through 3D reconstruction, and, lastly, the 3D object refining approach based on outlier object detection and replacement. The proposed methodology is implemented on a real‐time sample of nylon fibre bundle under compression and its 3D x‐ray image volume to validate the performance. The results show its superior performance compared to off‐the‐shelf image processing algorithms in terms of precision, that is, with a validation accuracy greater than 90%, and efficiency, that is, preventing the need for a huge data set and reducing the complexity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".