Increasing segmentation accuracy in ultrasound imaging using filtering and snakes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ultrasound images have low level of contrast and are corrupted with speckle noise. Due to these effects, segmentation of ultrasound images is very challenging. Because of their adaptive characteristics, active Contours or Snakes are a commonly used method for segmentation of this type of images. Even with this adaptive method which is made for this type of environment other challenges come across. With abundance of noise in ultrasound images, snakes cannot converge to the objectpsilas outline in some cases. As a result, the detected boundary will not be accurate enough. Therefore, some pre-processing methods are usually necessary. In this paper, contrast adjustment techniques and fusion of different filters have been implemented to help the snake algorithm converge. As a result, the boundaries of object of interest in this case prostate cancer will be identified. Then the accuracy is measured and compared with ground-truth images prepared by experts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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