Improved validation platform for ultrasound-based monitoring of thermal ablation
Bibliographic record
Abstract
PURPOSE: Thermal ablation is a popular method in local cancer management; however it is extremely challenging to predict thermal changes in vivo. Ultrasound could be a convenient and inexpensive imaging modality for real-time monitoring of the ablation, but the required advanced image processing algorithms need extensive validation. Our goal is to design and develop a reliable test-bed for validation of these monitoring algorithms. METHOD: We previously developed a test-bed, consisting of ablated tissue sample and fiducial lines embedded in tissue-mimicking gel.1 The gel block is imaged by ultrasound and sliced to acquire pathology images. Following fiducial localization in both image modalities, the pathology and US data were registered. Ground truth ablated region is retrieved from pathology images and compared to the result of the ultrasound-based processing in 3D space. We improved on this platform to resolve limitations that hindered its usage in a larger-scale validation study. A simulator for evaluating and optimizing different line fiducial structures was implemented, and a new fiducial line structure was proposed. RESULTS: The new proposed fiducial configuration outperforms the previous in terms of accuracy, fiducial visibility, and use of larger tissue samples. Simulation results show improvement in pose recovery accuracy using our proposed fiducial structure, reducing target registration error (TRE) by 34%. Inaccurate pixel spacing information and fiducial localization noise are the main sources of error in slice pose recovery. CONCLUSION: A new generation of test-bed was developed, with software that does not require lengthy manual data processing, and is easier to maintain and extend. Further experimental work is required to optimize phantom preparation and precise pixel spacing computation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".