Image Processing and Analysis of Histopathological Images Relating to Hirschsprung’s Disease
Why this work is in the frame
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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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it