Po‐Thur Eve General‐26: Tracking intraprostatic volumes for determining non‐linear warping algorithm variability
Bibliographic record
Abstract
Magnetic resonance spectroscopy imaging (MRSI) is becoming routinely used for diagnosis and treatment planning of prostate cancer. MRSI provides physicians and physicists with information related to metabolic activity of tissues within the prostate. During the MRSI data acquisition, an endorectal radio frequency coil is utilized to boost signal strength in the localized volume, and brings about a ∼10 fold increase in the signal to noise ratio. A challenge exist is using the MRSI data collected using the endorectal coil technique. The endorectal coil, while crucially important to data acquisition, is filled with approximately with ∼100cc's of air. This pushes the prostate superiorly/anteriorly, deforming the prostate and consequently the spectroscopic imaging data in a non‐linear manner. In this application, the coil‐deformed MRS images are warped back to a non‐deformed state, using a single data set. A non‐linear warping algorithm is presented to achieve this. For this case study, physicians were able to contour both the total prostate volume, and intraprostatic nodules. While choosing anatomical tie points along the external prostate surface (needed for the non‐linear warping algorithm), analysis of the nodules revealed the algorithm accuracy ranges from 63–93%. While the algorithm accuracy itself, when reproducing the total prostate volume, achieved an accuracy of 97%.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.006 | 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".