Development and evaluation of image preprocessing pipelines for the Centiloid method on Down Syndrome data
Bibliographic record
Abstract
BACKGROUND: Centiloid provides a standardized process to quantify brain amyloid in which a subject's T1 magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans are registered and warped to Montreal Neurological Institute 152 space using prescribed procedures. The method has a high failure rate in Down syndrome (DS) subjects from the Neurodegeneration in Aging Down Syndrome (NiAD) project. We evaluate imaging preprocessing methods (PMs) to improve the DS success rate. METHODS: PMs were constructed from combinations of image origin reset, filtering, MRI bias correction, and MRI skull stripping. Centiloid results were evaluated for adherence to standards using The Global Alzheimer's Association Interactive Network dataset. PMs were also evaluated using the NiAD dataset to judge their suitability for the DS population. DS PM evaluation procedures were developed corresponding to those specified for non-DS populations. RESULTS: Five accepted PMs improved the Centiloid-processing success rate in the DS cohort from 61.3% to 95.6%. DISCUSSION: The identified combinations of preprocessing steps substantially improved the success rate of Centiloid processing in DS. HIGHLIGHTS: Image preprocessing pipeline is proposed for Centiloid analysis of DS. Preprocessing pipelines are evaluated for adherence to Centiloid standards. Pipelines are evaluated for improvement in yield of usable imaging data. Preprocessing of amyloid imaging data resulted in a large yield improvement.
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 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".