Ground truth delineation for medical image segmentation based on Local Consistency and Distribution Map analysis
Bibliographic record
Abstract
Computer-aided detection (CAD) systems are being increasingly deployed for medical applications in recent years with the goal to speed up tedious tasks and improve precision. Among others, segmentation is an important component in CAD systems as a preprocessing step to help recognize patterns in medical images. In order to assess the accuracy of a CAD segmentation algorithm, comparison with ground truth data is necessary. To-date, ground truth delineation relies mainly on contours that are either manually defined by clinical experts or automatically generated by software. In this paper, we propose a systematic ground truth delineation method based on a Local Consistency Set Analysis approach, which can be used to establish an accurate ground truth representation, or if ground truth is available, to assess the accuracy of a CAD generated segmentation algorithm. We validate our computational model using medical data. Experimental results demonstrate the robustness of our approach. In contrast to current methods, our model also provides consistency information at distributed boundary pixel level, and thus is invariant to global compensation error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".