MATLAB-ITK interface for medical image filtering, segmentation, and registration
Bibliographic record
Abstract
To facilitate high level analysis of medical image data in research and clinical environments, a wrapper for the ITK toolkit is developed to allow ITK algorithms to be called in MATLAB. ITK is a powerful open-source toolkit implementing state-of-the-art algorithms in medical image processing and analysis. However, although ITK is rapidly gaining popularity, its user base is mostly restricted to technically savvy developers with expert knowledge of C++ and advanced programming concepts. MATLAB, on the other hand, is well-known for its easy-to-use, powerful prototyping capabilities that significantly improve productivity. Unfortunately, the 3D image processing capabilities of MATLAB are very limited and slow to execute. With the help of the wrapper we introduce in this paper, biomedical computing researchers familiar with MATLAB can harness the power of ITK while avoiding learning C++ and dealing with low-level programming issues. We strongly believe this functionality will be of considerable interest to the medical image computing community. In this paper we provide details about the design and usage of this interface in medical image filtering, segmentation, and registration.
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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 | 0.007 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.052 |
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".