Use of a variable thresholding-based image segmentation technique for magnetic resonance guided High Intensity Focused Ultrasound therapy: An in vivo validation
Bibliographic record
Abstract
In this paper, the implementation of a segmentation technique based on Otsu's method, region growing algorithm and selection of regions using global and variable thresholding for the treatment planning of Magnetic Resonance guided High Intensity Focused Ultrasound (MRgHIFU) is described. The method is used to classify the pixels of real Magnetic Resonance (MR) images obtained for the study of the distribution of heat in abscess treatment in a murine model with High-Intensity Focused Ultrasound (HIFU). Using a discrepancy measure for evaluation, the proposed technique (segmentation technique I) demonstrated to be more efficient than an approach technique (segmentation technique II) based on Otsu's method, global thresholding, edge detection and region growing algorithm. In the evaluation, a total of nine surveys of 48 images each were used. For axial images the performance of segmentation technique I and the performance of segmentation technique II is very similar, having an average value of 92.05% for the former and an average value of 91.45% for the latter. On the other hand, for sagittal images, segmentation technique I presented an average performance of 85.46% while segmentation technique II presented an average performance of 69.01%.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".