SAACT: Semiautomated Annotation of Computerized Tomography Data
Bibliographic record
Abstract
A successful deep learning-based solution design requires a large volume of well-annotated data to ensure model generalizability and efficient deployment. For certain advanced applications, such as semantic segmentation, the training dataset must be manually annotated by assigning labels to each pixel in the images. This labor-intensive and time-consuming process must be performed and verified by domain experts. This article presents a semiautomated data annotation technique for X-ray computed tomography (XCT) data, leveraging computer-aided design (CAD) design files. The proposed system employs various preprocessing techniques, including noise filtering and background removal. Additionally, we introduce an improved 3-D volume registration method based on the diffusion imaging in python (DIPY) library. The proposed annotation framework was applied to both real and semantic XCT datasets for an industrial tool and validated using a semantic segmentation model. The trained model achieved intersection over union (IoU) scores of 0.70 and 0.64 for the real and semantic XCT data, respectively. These results demonstrate the effectiveness of the annotation method, indicating strong performance in both cases. The findings confirm that the framework can be integrated into artificial intelligence (AI)-based industrial inspection systems to accelerate the industrial inspection processes, improve defect detection accuracy, and enable automated report generation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".