Image Processing and Analysis of Histopathological Images Relating to Hirschsprung’s Disease
Bibliographic record
Abstract
The current procedure for surgical treatment of Hirschsprung’s disease involves histopathological imaging of excised colon post-surgery by an expert pathologist, to confirm the complete removal of diseased colon. Pathologists examine slices of colon for the presence of neurons (ganglions) which may innervate the intestinal muscle. However, this practice is time-consuming and subjective, with evaluations varying between experts. The percentage of HD patients with pathology indications, whose symptoms persist post-operation, encourage experts to find an objective measure for the improvement in surgical outcome. In this preliminary study with ten patient cases from the Children’s Hospital of Eastern Ontario, we are proposing an image processing pipeline to segment the muscularis propria and myenteric plexus regions, as initial steps to identifying ganglions. We were able to segment the muscularis propria using a unsupervised k-means clustering algorithm with an average dice coefficient of 71.22% ± 20.44%. Digital Image Subtraction Blue Enhancement (DISBE) was used to identify myenteric plexus regions with a precision of 70.53% ± 28.08% when using the manual segmentations for the muscularis propria. Promising results encourage further development of these algorithms
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".