Fully-automated segmentation of the striatum in the PET/MR images using data fusion
Bibliographic record
Abstract
Different imaging modalities sample different properties of the tissue, and thus the tissue may appear different depending on the imaging technique. As a consequence, the shapes of organs and homogenous regions in tissues often have different shapes depending on the type of imaging. This presents a problem for ROI-based multi-modality quantitative imaging studies, since it is not clear what modality should be used for data segmentation. An example of such study is the quantitative PET imaging of Parkinson's disease subjects, which often present functional atrophy without an anatomical atrophy. A choice must be made between anatomical (MRI) and radioactivity-based (PET) ROIs. In addition manual ROI placement can be very time consuming and may lack consistency. In this work, we propose a new approach to multi-modality image segmentation. The proposed method generates so-called mixed ROIs that can be computed in a fully automated mode from single modality-based pure ROIs. The computation of the mixed ROIs is based on the fusion of probability images. The use of the fusion principles made it possible to transition between the pure ROI shapes in a smooth fashion. The mixed ROIs were found to be better aligned with the high activity regions than the pure MR ROIs, and had higher anatomical fidelity compared to the pure PET ROIs. Using the method, it is possible to generate a multitude of ROI sets for a particular study starting from one or more previously defined regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".