ArticleJournal of magnetic resonance imaging : JMRI2021
Multitask Learning Based Three-Dimensional Striatal Segmentation of MRI: fMRI and PET Objective Assessments.
Article in Journal of magnetic resonance imaging : JMRI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- DAT-SPECT-based subtype and stage inference in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- An interactive cascaded deep learning framework with expert refinement for accurate striatal subregion segmentation.Scientific reports · 2026Article
- Functional localization of the human auditory and visual thalamus using a thalamic localizer functional magnetic resonance imaging task.Imaging neuroscience (Cambridge, Mass.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundRecent 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.
resultsMTL-generated segmentations showed excellent spatial agreement with manual (DSC ≥0.72 across all striatal subregions). BP DATA
conclusionAcross 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.