UNIVERSITY OF CALGARY Accelerated Medical Image Registration using the Graphics Processing Unit
Bibliographic record
Abstract
Registration of three-dimensional images is an important task in biomedical sci-ence. However, computational costs of 3D registration algorithms have hindered their widespread use in many clinical and research workflows. We describe an automated medical image registration framework, including a novel implementation of the mu-tual information similarity metric, that executes entirely on the commodity graphics processing unit (GPU). Our methods take advantage of the graphics hardware’s high computational parallelism and memory bandwidth to perform ane, intensity-based registration of multi-modal 3D medical images at near interactive rates. We also ac-celerate the Demons algorithm for deformable registration on the GPU. Registration results generated using our GPU-based methods are equivalent to those generated by conventional software-based methods, but with an order of magnitude reduction in computation time. ii Acknowledgements I thank my lab mates, supervisor, and close friends and family for their support throughout my graduate studies. In particular, I thank Sonny Chan and Eric Penner for their mentorship and innovative ideas in image registration and computer graphics. iii
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.017 |
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".