Evidence map›Paper›PMID 39698481›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2023

Deformable Spherical Transformer for Cerebellar Surface Parcellation.

Jiale Cheng, Fenqiang Zhao, Zhengwang Wu, Ya Wang, Xinrui Yuan, Yue Sun, Weili Lin, Li Wang, Xin Zhang, Gang Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jiale ChengSchool of Electronic and Information Engineering, South China University of Technology, China.
Fenqiang ZhaoDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Zhengwang WuDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Ya WangDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Xinrui YuanDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Yue SunDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Weili LinDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Li WangDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Xin ZhangSchool of Electronic and Information Engineering, South China University of Technology, China.
Gang LiDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.

Funding

Continued Development of Infant Brain Analysis ToolsR01MH117943 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG, WANG, LI · 2018 to 2022
$2.3M
Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRIR01MH116225 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2018 to 2022
$1.9M
Harmonizing and Archiving of Large-scale Infant Neuroimaging DataRF1MH123202 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2021 to 2021
$627k
Infant Functional Connectome Fingerprinting based on Deep LearningR21MH127544 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GANG · 2021 to 2022
$272k
NIMH NIH HHS R01 MH116225NIMH NIH HHS R01 MH117943NIMH NIH HHS R21 MH127544NIMH NIH HHS RF1 MH123202
6 · The paper itself

Abstract

In neuroimaging analysis, accurate parcellation of the extremely folded cerebellar cortex is of immense importance for both structural and functional studies. To this end, we aim to develop a novel end-to-end deep learning-based method for automatic parcellation of the cerebellar cortical surface, which has an intrinsic spherical topology. Motivated by the success of Transformer, we employ Spherical Transformer to leverage its ability to model long-range dependency. To address the nonuniform, moderate distortions during the spherical mapping of the folded cerebellar surface, we propose a Deformable Spherical Transformer, which combines the Spherical Transformer architecture with the deformable attention mechanism to adaptively concentrate on the critical and challenging regions on the spherical cerebellar surface. By comparing with other state-of-the-art algorithms, we validated the superior performance of our proposed methods.

Indexed as

CerebellumDeformable MechanismParcellationTransformer

Identifiers

PMID39698481
PMCPMC11655068

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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.