Evidence map›Paper›PMID 34290489›Full record

ArticleInformation processing in medical imaging : proceedings of the ... conference2021

Cortical Morphometry Analysis based on Worst Transportation Theory.

Min Zhang, Dongsheng An, Na Lei, Jianfeng Wu, Tong Zhao, Xiaoyin Xu, Yalin Wang, Xianfeng Gu

Abstract read
In one paragraph

Article in Information processing in medical imaging : proceedings of the ... conference, 2021. 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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1 · What the graph read from it

What it found

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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

8 authors.

Min Zhang *Zhejiang University.
Dongsheng An *Department of Computer Science, Stony Brook University, Brookhaven, USA.
Na LeiDUT-RU ISE, Dalian University of Technology.
Jianfeng WuSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Arizona, USA.
Tong ZhaoUniversité Côte d'Azur, Inria.
Xiaoyin XuBrigham and Women's Hospital, Harvard Medical School.
Yalin WangSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Arizona, USA.
Xianfeng GuDepartment of Computer Science, Stony Brook University, Brookhaven, USA.

Funding

Multi-Source Sparse Learning to Identify MCI and Predict DeclineRF1AG051710 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THOMPSON, PAUL M, WANG, YALIN · 2016 to 2016
$2.8M
Predicting the early childhood outcomes of preterm brain shape abnormalitiesR01EB025032 · NIBIB · CHILDREN'S HOSPITAL OF LOS ANGELES · PI LEPORE, NATASHA · 2017 to 2020
$2.6M
Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal TransportationR21AG065942 · NIA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI WANG, YALIN · 2020 to 2020
$445k
NIA NIH HHS R21 AG065942NIA NIH HHS RF1 AG051710NIBIB NIH HHS R01 EB025032
6 · The paper itself

Abstract

Biomarkers play an important role in early detection and intervention in Alzheimer's disease (AD). However, obtaining effective biomarkers for AD is still a big challenge. In this work, we propose to use the worst transportation cost as a univariate biomarker to index cortical morphometry for tracking AD progression. The worst transportation (WT) aims to find the least economical way to transport one measure to the other, which contrasts to the optimal transportation (OT) that finds the most economical way between measures. To compute the WT cost, we generalize the Brenier theorem for the OT map to the WT map, and show that the WT map is the gradient of a concave function satisfying the Monge-Ampere equation. We also develop an efficient algorithm to compute the WT map based on computational geometry. We apply the algorithm to analyze cortical shape difference between dementia due to AD and normal aging individuals. The experimental results reveal the effectiveness of our proposed method which yields better statistical performance than other competiting methods including the OT.

Indexed as

Alzheimer’s diseaseShape analysisWorst transportation

Identifiers

PMID34290489
PMCPMC8291742

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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.