Multitask Learning Based Three-Dimensional Striatal Segmentation of MRI: fMRI and PET Objective Assessments.

Brain Corpus Striatum / diagnostic imaging Humans Magnetic Resonance Imaging Positron-Emission Tomography Retrospective Studies
MRI PET multitask learning striatal segmentation

Journal

Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850

Informations de publication

Date de publication:
11 2021
Historique:
revised: 22 04 2021
received: 05 03 2021
accepted: 23 04 2021
pubmed: 11 5 2021
medline: 16 10 2021
entrez: 10 5 2021
Statut: ppublish

Résumé

Recent studies have established a clear topographical and functional organization of projections to and from complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, and automated methods are not as reliable as manual segmentation. To utilize multitask learning (MTL) as a method to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST). Retrospective. Eighty-seven total data sets from patients with schizophrenia and matched controls. 1.5 T and 3.0 T, T MTL-generated segmentations were compared to the Imperial College London Clinical Imaging Center (CIC) atlas. Dice similarity coefficient (DSC) was used to compare the automated methods to manual segmentations. Positron emission tomography (PET) imaging: 60 minutes of emission data were acquired using [ Pearson correlation and paired t-test. MTL-generated segmentations showed excellent spatial agreement with manual (DSC ≥0.72 across all striatal subregions). BP Across both PET and fMRI task-based assessments, results from MTL-generated segmentations more closely corresponded to results from manually drawn ROIs than CIC-generated segmentations did. Therefore, the proposed MTL approach is a fast and reliable method for three-dimensional striatal subregion segmentation with results comparable to manually segmented ROIs. 2 TECHNICAL EFFICACY STAGE: 1.

Sections du résumé

BACKGROUND
Recent studies have established a clear topographical and functional organization of projections to and from complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, and automated methods are not as reliable as manual segmentation.
PURPOSE
To utilize multitask learning (MTL) as a method to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST).
STUDY TYPE
Retrospective.
POPULATION
Eighty-seven total data sets from patients with schizophrenia and matched controls.
FIELD STRENGTH/SEQUENCE
1.5 T and 3.0 T, T
ASSESSMENT
MTL-generated segmentations were compared to the Imperial College London Clinical Imaging Center (CIC) atlas. Dice similarity coefficient (DSC) was used to compare the automated methods to manual segmentations. Positron emission tomography (PET) imaging: 60 minutes of emission data were acquired using [
STATISTICAL TESTS
Pearson correlation and paired t-test.
RESULTS
MTL-generated segmentations showed excellent spatial agreement with manual (DSC ≥0.72 across all striatal subregions). BP
DATA CONCLUSION
Across both PET and fMRI task-based assessments, results from MTL-generated segmentations more closely corresponded to results from manually drawn ROIs than CIC-generated segmentations did. Therefore, the proposed MTL approach is a fast and reliable method for three-dimensional striatal subregion segmentation with results comparable to manually segmented ROIs.
LEVEL OF EVIDENCE
2 TECHNICAL EFFICACY STAGE: 1.

Identifiants

DOI: 10.1002/jmri.27682 PMID: 33970510 PMC: PMC9204799
pubmed: 33970510
doi: 10.1002/jmri.27682
pmc: PMC9204799
mid: NIHMS1812380
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1623-1635

Subventions

Organisme : NIMH NIH HHS
ID : K01 MH107763
Pays : United States
Organisme : NIMH NIH HHS
ID : K23 MH115291
Pays : United States
Organisme : NIMH NIH HHS
ID : P50 MH086404
Pays : United States
Organisme : NIMH NIH HHS
ID : R21 MH099509
Pays : United States

Informations de copyright

© 2021 International Society for Magnetic Resonance in Medicine.

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Auteurs

Mario Serrano-Sosa (M)

Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.

Jared X Van Snellenberg (JX)

Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.
Department of Psychiatry and Behavioral Health, Stony Brook Medicine, Stony Brook, New York, USA.
Department of Psychology, Stony Brook University, Stony Brook, New York, USA.

Jiayan Meng (J)

Department of Psychiatry and Behavioral Health, Stony Brook Medicine, Stony Brook, New York, USA.

Jacob R Luceno (JR)

Department of Psychiatry and Behavioral Health, Stony Brook Medicine, Stony Brook, New York, USA.

Karl Spuhler (K)

Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.

Jodi J Weinstein (JJ)

Department of Psychiatry and Behavioral Health, Stony Brook Medicine, Stony Brook, New York, USA.

Anissa Abi-Dargham (A)

Department of Psychiatry and Behavioral Health, Stony Brook Medicine, Stony Brook, New York, USA.

Mark Slifstein (M)

Department of Psychiatry and Behavioral Health, Stony Brook Medicine, Stony Brook, New York, USA.

Chuan Huang (C)

Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.
Department of Psychiatry and Behavioral Health, Stony Brook Medicine, Stony Brook, New York, USA.
Department of Radiology, Stony Brook Medicine, Stony Brook, New York, USA.
ORCID: 0000-0001-6052-0663

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