Assessment of brain surface extraction from PET images using Monte Carlo Simulations
Bibliographic record
Abstract
In this paper, we evaluate quantitatively the performance of the fully automatic deformable model with dual surface minimization (DM-DSM) method for brain surface extraction from positron emission tomography (PET) images with Monte Carlo simulated data. In addition, we cross validate the DM-DSM method with a method based on MRI-PET registration for PET brain delineation. For this, the automated image registration (AIR) algorithm is combined with the anatomical brain surface extractor (BSE) algorithm. Two radiopharmaceuticals were considered: C-11-Raclopride and F-18-FDG. The success of the two methods was quantified by measuring the similarity between the extracted and the true brain volume. Also local differences between the extracted and the true brain surfaces were measured. With FDG, the DM-DSM method yielded brain surfaces of high accuracy and they were more accurate than with the image registration based method. With Raclopride, the accuracy of the DM-DSM method was slightly lower than with FDG and, on the average, similar to the accuracy of the image registration based method. However with Raclopride, maximal local differences between true and extracted surfaces were found to be greater with DM-DSM. In addition, preliminary experiments with images containing simulated pathology were done and the performance of DM-DSM was excellent in these experiments. To summarize, we found that the DM-DSM method can reliably extract brain surfaces of high accuracy from PET images.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".