Area prior constrained level set evolution for medical image segmentation
Bibliographic record
Abstract
The level set framework has proven well suited to medical image segmentation<sup>1-6</sup> thanks to its ability of balancing the contribution of image data and prior knowledge in a principled, flexible and transparent way. It consists of evolving a curve toward the target object boundaries. The curve evolution equation is sought following the optimization of a cost functional containing two types of terms: data terms, which measure the fidelity of segmentation to image intensities, and prior terms, which traduce learned prior knowledge. Without priors many algorithms are likely to fail due to high noise, low contrast and data incompleteness. Different priors have been investigated such as shape<sup>1</sup> and appearance priors.<sup>7</sup> In this study, we propose a simple type of priors: the area prior. This prior embeds knowledge of an approximate object area and has two positive effects. First, It speeds up significantly the evolution when the curve is far from the target object boundaries. Second, it slows down the evolution when the curve is close to the target. Consequently, it reinforces curve stability at the desired boundaries when dealing with low contrast intensity edges. The algorithm is validated with several experiments using Magnetic Resonance (MR) images and Computed Tomography (CT) images. A comparison with another level set method illustrates the positive effects of the area prior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".