Bibliographic record
Abstract
Prostate brachytherapy is a widely used treatment of localized prostate cancer. Intra-operative dose feedback would bring many benefits to patients and healthcare practitioners. Detection of brachytherapy seeds and segmentation of prostate boundaries play key roles in dosimetry for prostate brachytherapy. However, seed detection and prostate segmentation using conventional B-mode transrectal ultrasound still remains a challenge for prostate brachytherapy, mainly due to the small size of brachytherapy seeds in the relatively low-quality B-mode ultrasound images and due to the poor contrast between the prostate gland and surrounding tissues, speckle noise, shadowing and refraction artifacts. In this thesis, a new method called the reflected power imaging is presented to enhance the visibility and imaging of implanted seeds. It directly measures the reflected energy of ultrasound radio-frequency signals without logarithmic compression. Based on this method, we propose a new solution for brachytherapy seed detection in a 3D reflected power image computed from ultrasound radio-frequency signals, instead of conventional B-mode images. Then implanted seeds are segmented in 3D local search spaces that are determined by α priori knowledge, e.g. needle entry points and seed placements in a pre-operative dosimetry plan. Needle insertion tracks are also detected locally by using the Hough Transform. Experimental results show that the proposed solution works well for seed localization in a tissue-equivalent ultrasound prostate phantom implanted according to a realistic treatment plan with 136 seeds from 26 needles. As the prostate is a firm organ relative to surrounding tissues, elastography is a potential imaging modality for the guidance of prostate brachytherapy. A dynamic ultrasound elastography method named vibro-elastography can provide more complete dynamic tissue description in terms of transfer functions and coherence functions. In this thesis, we develop fast computational algorithms and programs to implement vibro-elastography imaging in real time. Phantom experiments demonstrate that the vibro-elastography techniques produce stable and operator-independent strain images with high contrast-to-noise ratio. Furthermore, the software for a 3D vibro-elastography imaging system has been designed, implemented and used in the data collection. Over 15 patients have been scanned at the British Columbia Cancer Agency, Vancouver Centre, and the results are encouraging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".