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Record W4413006736 · doi:10.1148/ryai.240478

Automated Deep Learning–based Segmentation of the Dentate Nucleus Using Quantitative Susceptibility Mapping MRI

2025· article· en· W4413006736 on OpenAlexaff
Diogo H. Shiraishi, Susmita Saha, Isaac Adanyeguh, Sirio Cocozza, Louise A. Corben, Andreas Deistung, Martin B. Delatycki, Imis Dogan, William Gaetz, Nellie Georgiou‐Karistianis, Simon Graf, Marina Grisoli, Pierre‐Gilles Henry, Gustavo M. Jarola, James M. Joers, Christian Langkammer, Christophe Lenglet, Jiakun Li, Camila Caroso Lobo, Eric F. Lock, David R. Lynch, Thomas H. Mareci, Alberto Martínez, Serena Monti, Anna Nigri, Massimo Pandolfo, Kathrin Reetz, Timothy P. L. Roberts, Sandro Romanzetti, David A. Rudko, Alessandra Scaravilli, Jörg B. Schulz, S. H. Subramony, Dagmar Timmann, Marcondes C. França, Ian H. Harding, Thiago Junqueira Ribeiro de Rezende

Bibliographic record

VenueRadiology Artificial Intelligence · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMontreal Neurological Institute and HospitalMcGill University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNational Health and Medical Research CouncilNational Institutes of HealthFriedreich's Ataxia Research Alliance
KeywordsIntraclass correlationSegmentationSørensen–Dice coefficientArtificial intelligenceDeep learningDentate nucleusComputer scienceMagnetic resonance imagingMedicinePattern recognition (psychology)Image segmentationPsychologyRadiologyCerebellumNeuroscience

Abstract

fetched live from OpenAlex

A deep learning model using a two-step localization and segmentation pipeline accurately and reliably segmented the dentate nucleus using brain MRI–based quantitative susceptibility mapping 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.066
GPT teacher head0.401
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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