Automated Segmentation and Quantification of Histology Fragments for Enhanced Macroscopic Reporting
Bibliographic record
Abstract
Manual tissue documentation is a critical step in the field of pathology that sets the stage for microscopic analysis and significantly influences diagnostic outcomes. In routine practice, technicians verbally dictate descriptions of specimens during gross examination; these are later transcribed into macroscopic reports. Fragment sizes are measured manually with rulers; however, these measurements are often inconsistent for small, irregular biopsies. No photographic record is captured for traceability. To address these limitations, we propose a proof-of-concept framework that automates the image capture and documentation of biopsy and resection cassettes. It integrates a custom imaging platform and a segmentation pipeline leveraging the YOLOv8 and YOLOv9 architectures to improve accuracy and efficiency. The framework was tested in a real clinical context and was evaluated on two datasets of 100 annotated images each, achieving a mask mean Average Precision (mAP) of 0.9517 ± 0107 and a tissue fragment spatial accuracy of 96.20 ± 1.37%. These results demonstrate the potential of our framework to enhance the standardization, reliability, and speed of macroscopic documentation, contributing to improved traceability and diagnostic precision.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".