Deep-learning segmentation of the substantia nigra from multiparametric MRI: Application to Parkinson’s disease
Bibliographic record
Abstract
with NM-sensitive MRI, showing potential as a biomarker of PD. This relies on delineating the NM-rich region, which is achieved by applying manual or automated methods. Currently, there is a lack of publicly available tools for this task, so we trained a deep neural network intended for publishing, while exploring the effects of incorporating multiparametric MRI for segmenting the NM hyperintensity of the SN. We obtained multiple MRI contrasts, including NM-sensitive magnetization transfer contrast from 109 individuals (87 PD, 22 healthy controls) comprising a Norwegian and a Canadian cohort. The method was further evaluated on 209 MRIs from the Parkinson's Progressive Markers Initiative (PPMI). We observed that models trained naively on images from a single site tended to perform very poorly when exposed to similar data from different sites, emphasizing the importance of validating on out-of-distribution data. By applying aggressive data augmentation, we could largely attenuate the problem. We also observed a small additional regularizing effect from training the neural network on multiparametric MRIs. Volume and contrast-to-noise ratio (CNR) of the SN hyperintensity to the crus cerebri were used to distinguish patients from controls, with an area under the receiver operating characteristic (AUROC) of 0.863. CNR was found to be a better marker of disease status than volume, and we discuss a potential confusion in discerning the two measures. No contralateral association was observed between the severity of motor symptoms and volume or CNR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".