Evidence map›Paper›PMID 40543276›Full record

ArticleComputers in biology and medicine2025

Three-dimensional U-Net with transfer learning improves automated whole brain delineation from MRI brain scans of rats, mice, and monkeys.

Valerie A Porter, Brad A Hobson, Alita J D'Almeida, Karen L Bales, Pamela J Lein, Abhijit J Chaudhari

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PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Valerie A PorterDepartment of Biomedical Engineering, University of California, Davis, One Shields Ave, Davis, CA, USA; Department of Radiology, University of California, Davis, One Shields Ave, Davis, CA, USA.
Brad A HobsonDepartment of Biomedical Engineering, University of California, Davis, One Shields Ave, Davis, CA, USA; Center for Molecular and Genomic Imaging, University of California, Davis, One Shields Ave, Davis, CA, USA.
Alita J D'AlmeidaDepartment of Biomedical Engineering, University of California, Davis, One Shields Ave, Davis, CA, USA; Department of Radiology, University of California, Davis, One Shields Ave, Davis, CA, USA.
Karen L BalesDepartment of Psychology, University of California, Davis, One Shields Ave, Davis, CA, USA; California National Primate Research Center, One Shields Ave, Davis, CA, USA.
Pamela J LeinDepartment of Molecular Biosciences, University of California, Davis, One Shields Ave, Davis, CA, USA.
Abhijit J ChaudhariDepartment of Radiology, University of California, Davis, One Shields Ave, Davis, CA, USA; Center for Molecular and Genomic Imaging, University of California, Davis, One Shields Ave, Davis, CA, USA; California National Primate Research Center, One Shields Ave, Davis, CA, USA. Electronic address: ajchaudhari@ucdavis.edu.

Funding

National Institute on Aging (NIA) ColonyP51OD011107 · OD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Simon J. Atkinson · 2012 to 2026
$191.5M
UC Davis Clinical and Translational Science CenterUL1TR001860 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KENYON, NICHOLAS J., LYLES, COURTNEY REES · 2016 to 2025
$47.3M
Training and Education CoreU54NS079202 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI LEIN, PAMELA J · 2012 to 2021
$35.7M
UC Davis CounterACT Center of Excellence: Developing Therapeutic Strategies for Mitigating the Chronic Neurological Consequences of Acute Organophosphate IntoxicationU54NS127758 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Amy R. Brooks-Kayal, Pamela J Lein · 2022 to 2026
$17.2M
UC Davis CTSC TL1 Administrative Supplement to Recognize Excellence in Diversity, Equity, Inclusion, and Accessibility MentorshipTL1TR001861 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI MEDICI, VALENTINA · 2016 to 2025
$4.7M
Kappa opioid and oxytocin interactions in social buffering and separationR01MH125411 · NIMH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BALES, KAREN L. · 2021 to 2025
$3.6M
Total-body PET for assessing myofascial painR33AT012187 · NCCIH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CHAUDHARI, ABHIJIT J, NARDO, LORENZO · 2025 to 2025
$2.5M
Total-body PET for assessing myofascial painR61AT012187 · NCCIH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CHAUDHARI, ABHIJIT J, NARDO, LORENZO · 2022 to 2022
$2.1M
Multimodality PET/CT Scanner for Small Animal ImagingS10OD030440 · OD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CHAUDHARI, ABHIJIT J · 2022 to 2022
$878k
piPET: A positron emission tomography (PET) scanner for nonhuman primate brain imagingS10OD021715 · OD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CHERRY, SIMON R · 2016 to 2016
$594k
Imaging biomarkers of early synaptic changes in a preclinical model of Alzheimer’s diseaseR21AG064599 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CHAUDHARI, ABHIJIT J, LEIN, PAMELA J · 2019 to 2020
$432k
Does air pollution increase risk of AD in a genetically susceptible animal model?R21ES026515 · NIEHS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI LEIN, PAMELA J · 2016 to 2017
$432k
NCATS NIH HHS TL1 TR001861NCATS NIH HHS UL1 TR001860NCCIH NIH HHS R33 AT012187NCCIH NIH HHS R61 AT012187NIA NIH HHS R21 AG064448NIA NIH HHS R21 AG064599NIEHS NIH HHS R21 ES026515NIH HHS P51 OD011107NIH HHS S10 OD021715NIH HHS S10 OD030440NIMH NIH HHS R01 MH125411NINDS NIH HHS U54 NS079202NINDS NIH HHS U54 NS127758
6 · The paper itself

Abstract

backgroundAutomated whole-brain delineation (WBD) techniques often struggle to generalize across pre-clinical studies due to variations in animal models, magnetic resonance imaging (MRI) scanners, and tissue contrasts. We developed a 3D U-Net neural network for WBD pre-trained on organophosphate intoxication (OPI) rat brain MRI scans. We used transfer learning (TL) to adapt this OPI-pretrained network to other animal models: rat model of Alzheimer's disease (AD), mouse model of tetramethylenedisulfotetramine (TETS) intoxication, and titi monkey model of social bonding.

methodsWe assessed an OPI-pretrained 3D U-Net across animal models under three conditions: (1) direct application to each dataset; (2) utilizing TL; and (3) training disease-specific U-Net models. For each condition, training dataset size (TDS) was optimized, and output WBDs were compared to manual segmentations for accuracy.

resultsThe OPI-pretrained 3D U-Net (TDS = 100) achieved the best accuracy [median[min-max]] for the test OPI dataset with a Dice coefficient (DC) = [0.987 [0.977-0.992]] and Hausdorff distance (HD) = [0.86 [0.55-1.27]]mm. TL improved generalization across all models [AD (TDS = 40): DC = 0.987 [0.977-0.992] and HD = 0.72 [0.54-1.00]mm; TETS (TDS = 10): DC = 0.992 [0.984-0.993] and HD = 0.40 [0.31-0.50]mm; Monkey (TDS = 8): DC = 0.977 [0.968-0.979] and HD = 3.03 [2.19-3.91]mm], showing performance comparable to disease-specific networks.

conclusionsThe OPI-pretrained 3D U-Net with TL achieved accuracy comparable to disease-specific networks with reduced training data (TDS ≤ 40 scans) across all models. Future work will focus on developing a multi-region delineation pipeline for pre-clinical MRI brain data, utilizing the proposed WBD as an initial step.

Indexed as

BrainImaging, Three-DimensionalMagnetic Resonance ImagingNeural Networks, ComputerAlzheimer DiseaseAnimalsDisease Models, AnimalMaleMiceRatsBrain segmentationConvolutional neural networkMagnetic resonance imagingPreclinical modelsTransfer learning

Identifiers

PMID40543276
PMCPMC12237433

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.