Automated Growcut for segmentation of endoscopic images
Bibliographic record
Abstract
Capsule endoscopy (CE), introduced as a modality for non-invasive examination of entire gastrointestinal tract, demands for an efficient computer-aided decision making system to relieve the physician from the responsibility of screening around 60,000 video frames per patient. An automatic and robust segmentation algorithm can aid the automation of CE screening and decision making procedure. In this paper, we propose a new segmentation algorithm based on GrowCut and apply the algorithm for CE images containing bleeding. To substitute the manual seed input in traditional GrowCut segmentation, the proposed Automated GrowCut (AGC) algorithm initially segments the images using clustering. The cluster centroids, subsequently labeled as bleeding, non-bleeding and background by a trained SVM classifier, serve as seeds for the GrowCut segmentation. A comprehensive evaluation and comparison with respect to ground truth exhibits that the proposed method can achieve a Dice Similarity Coefficient of 0.81, comparable to the interactive GrowCut, requiring only 13.96% of the computation time of interactive GrowCut. The comparison with two other state-of-the-art unsupervised segmentation methods, unsupervised GrowCut and Fuzzy c-means, further justifies the suitability of the proposed method for automated segmentation and annotation of CE images.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".