Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
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.
2 · The registry
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.
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
7 authors.
Kathleen E LarsonDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0002-1997-0248
Jean C AugustinackDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Jocelyn MoraDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Devani ShahzadeDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Otto RapalinoDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Bruce FischlDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Douglas N GreveDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Funding
Functionally guided adult whole brain cell atlas in human and NHPUM1MH130981 · NIMH · ALLEN INSTITUTE · PI Ed Lein, Hongkui Zeng · 2022 to 2026
$91.9M
BRAIN CONNECTS: The center for Large-scale Imaging of Neural Circuits (LINC)UM1NS132358 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Suzanne N Haber, Elizabeth M. C. Hillman · 2023 to 2026
$17.5M
EPIDEMIOLOGY OF DEMENTIA IN THE FRAMINGHAM STUDYR01AG008122 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, SESHADRI, SUDHA · 1989 to 2015
$14.4M
MRI, Genetics &Cognitive Precursors of AD &DementiaR01AG016495 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA · 1999 to 2016
$12.5M
The Human Connectome Project (HCP)U01MH093765 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2010 to 2014
$12.1M
Training and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BRUCE R ROSEN · 2020 to 2026
$10.9M
Imaging and Analysis Techniques to Construct a Cell Census Atlas of the Human Brain Admin SupplementU01MH117023 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI BOAS, DAVID A, FISCHL, BRUCE · 2018 to 2022
$7.6M
Neuropathology of CTE and Delayed Effects of TBI: Toward In-Vivo DiagnosticsU01NS086625 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DAMS-O'CONNOR, KRISTEN · 2014 to 2017
$6.1M
FreeSurfer Development, Maintenance, and HardeningR01EB023281 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl, Douglas Nowlin Greve · 2016 to 2026
$5.6M
A Longitudinal Analysis Stream for FreeSurferR01NS083534 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl · 2014 to 2026
$4.6M
BRAIN CONNECTS: Mapping Connectivity of the Human Brainstem in a Nuclear Coordinate SystemU01NS132181 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE, HOF, PATRICK R · 2023 to 2025
$4.2M
Deep Learning Algorithms for FreeSurferR01AG064027 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2020 to 2024
$3.4M
BRAIN CONNECTS: The center for Large-scale Imaging of Neural Circuits (LINC) 1UM1NS132358-01BRAIN CONNECTS: The center for Large-scale Imaging of Neural Circuits (LINC) U01NS132181Brain Initiative Cell Census Network U01-MH117023Brain Initiative Cell Census Network UM1-MH130981National Institute of Biomedical Imaging and Bioengineering (NIBIB) 1R01EB023281National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41EB030006National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB006758National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB019956National Institute of Biomedical Imaging and Bioengineering (NIBIB) R21EB018907NCRR NIH HHS S10 RR019307NCRR NIH HHS S10 RR023043NCRR NIH HHS S10 RR023401NIA NIH HHS 1R01AG064027NIA NIH HHS 1R01AG070988NIA NIH HHS 5R01AG008122NIA NIH HHS R01 AG008122NIA NIH HHS R01 AG016495NIA NIH HHS R01AG016495NIA NIH HHS R01 AG064027NIA NIH HHS R01 AG070988NIA NIH HHS R21 AG082082NIA NIH HHS R21AG082082NIBIB NIH HHS P41 EB030006NIBIB NIH HHS R01 EB006758NIBIB NIH HHS R01 EB019956NIBIB NIH HHS R01 EB023281NIBIB NIH HHS R21 EB018907NIH Blueprint for Neuroscience Research 5U01MH093765NIH Shared Instrumentation 1S10RR019307NIH Shared Instrumentation 1S10RR023043NIH Shared Instrumentation 1S10RR023401NIMH NIH HHS 1RF1MH123195NIMH NIH HHS R01MH121885NIMH NIH HHS R01MH123195NIMH NIH HHS RF1 MH121885NIMH NIH HHS RF1 MH123195NIMH NIH HHS U01 MH093765NIMH NIH HHS U01 MH117023NIMH NIH HHS UM1 MH130981NIMH NIH HHS UM1MH130981NINDS NIH HHS 2R01NS083534NINDS NIH HHS 5U01NS086625NINDS NIH HHS 5U24NS10059103NINDS NIH HHS R01NS0525851NINDS NIH HHS R01 NS070963NINDS NIH HHS R01NS070963NINDS NIH HHS R01 NS083534NINDS NIH HHS R01 NS105820NINDS NIH HHS R01NS105820NINDS NIH HHS R21 NS072652NINDS NIH HHS R21NS072652NINDS NIH HHS U01 NS086625NINDS NIH HHS U01 NS132181NINDS NIH HHS UM1 NS132358
6 · The paper itself
Abstract
The pituitary and pineal glands are two small yet critical brain structures that help to modulate the human endocrine system. Unfortunately, very little research has been devoted to segmenting the pineal gland, and existing methods for pituitary segmentation focus only on the entire gland without distinguishing between its two lobes. To fill this gap, this work presents the first deep-learning-based tool for segmentation of both the pineal and pituitary glands in T1-weighted MRI. A five-fold cross-validation study was conducted on a manually labeled training dataset and produced segmentations with accuracy comparable to similar methods for segmenting other small brain structures. Model performance was then tested in three publicly available datasets using a total of n = 816 subjects, the results of which were both highly reproducible and robust to differences in MRI scanners and acquisition protocols. Finally, an analysis was performed to identify group differences related to sex and the diagnosis of schizophrenia and showed that volumes measured from the output segmentations were effective at discerning sex- and disease case-related differences in the pituitary and pineal glands.
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.
Automated Segmentation of the Pituitary and Pineal Glands. · full record | OpenQuestion