Multi-modality fusion of CT, 3D ultrasound, and tracked strain images for breast irradiation planning
Bibliographic record
Abstract
Breast irradiation significantly reduces the risk of recurrence of cancer. There is growing evidence suggesting that irradiation of only the involved area of the breast, partial breast irradiation (PBI), is as effective as whole breast irradiation. Benefits of PBI include shortened treatment time, and perhaps fewer side effects as less tissue is treated. However, these benefits cannot be realized without precise and accurate localization of the lumpectomy cavity. Several studies have shown that accurate delineation of the cavity in CT scans is very challenging and the delineated volumes differ dramatically over time and among users. In this paper, we propose utilizing 3D ultrasound (3D-US) and tracked strain images as complementary modalities to reduce uncertainties associated with current CT planning workflow. We present the early version of an integrated system that fuses 3D-US and real-time strain images. For the first time, we employ tracking information to reduce the noise in calculation of strain image by choosing the properly compressed frames and to position the strain image within the ultrasound volume. Using this system, we provide the tools to retrieve additional information from 3D-US and strain image alongside the CT scan. We have preliminarily evaluated our proposed system in a step-by-step fashion using a breast phantom and clinical experiments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".