Biomechanical registration of prostate images using statistical shape models
Bibliographic record
Abstract
Proper targeting of radiation therapy during the treatment of prostate cancer requires the successful alignment of initial planning images with more recent ones taken during the treatment course. Prostatic displacement, if unaccounted for during the treatment course, can lead to radiation underdosage of the target area or radiation of the surrounding healthy tissue. Studies have shown that prostatic displacement is directly correlated with changes in the volume of the rectum. We have developed a non-rigid image registration system based on a biomechanical model of the prostate, rectum, and surrounding tissues, that incorporates statistical shape information about changes in the volume of the rectum. Using finite-element analysis and statistical shape models, our non-rigid registration method defines a mapping between two prostate image volumes. The proposed method assumes that the prostate image misregistration occurs as a result of changes in the rectum's shape. The change along the rectum's circumference was considered as the displacement boundary condition of the prostate's finite element model. As such we used a mutual information similarity measure in conjunction with the finite element model for computing the optimal boundary condition as well as estimating the location and relative Young's modulus of the central and peripheral zones within the prostate to the surrounding tissue. Compared to other techniques, this registration technique is not only efficient but also capable of providing valuable mechanical properties of tissue in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".