An automated system for registration and fusion of 3D ultrasound images during cervical brachytherapy procedures
Bibliographic record
Abstract
High dose-rate brachytherapy is a typical part of the treatment process for cervical cancer. During this procedure, radioactive sources are placed locally to the malignancy using specialized applicators or interstitial needles. To ensure accurate dose delivery and positive patient outcomes, medical imaging is utilized intra-procedurally to ensure precise placement of the applicator. Previously, the fusion of three-dimensional ultrasound images has been investigated as an alternative volumetric imaging technique during cervical brachytherapy treatments. However, the need to manually register the two three-dimensional ultrasound images offline resulted in excessively large registration errors. To overcome this limitation, we have designed and developed a tracked, automated mechatronic system to inherently register three-dimensional ultrasound images in real-time. We perform a system calibration using an external coordinate system transform and validate the system tracking using a commercial optical tracker. The results of both experiments indicated sub-millimeter system accuracy, indicating the superior performance of our device. Future work for this study includes performing phantom validation experiments and translating our device into clinical work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 0.002 |
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".