Evidence map›Paper›PMID 38013951›Full record

ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2022

Using Optimal Transport to Improve Spherical Harmonic Quantification of Complex Biological Shapes.

Zexuan Wang, Wenxi Yang, Katharine Ryan, Sumita Garai, Benjamin M Auerbach, Li Shen

Abstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

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

6 authors.

Zexuan WangApplied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, USA.
Wenxi YangApplied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, USA.
Katharine RyanDepartment of Biology, Sacred Heart University, Fairfield Connecticut, USA.
Sumita GaraiDepartment of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.
Benjamin M AuerbachDepartment of Anthropology, The University of Tennessee, Knoxville, Tennessee, USA.
Li ShenDepartment of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.

Funding

Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Novel integrative imaging genetics analysis for Alzheimer's disease riskand progressionRF1AG068191 · NIA · YALE UNIVERSITY · PI ZHAO, YIZE · 2021 to 2021
$1.9M
NIA NIH HHS RF1 AG068191NIA NIH HHS U01 AG068057
6 · The paper itself

Abstract

The knowledge of the anatomical shape of both gross and microscopic structures is the key to understanding the effects of disease processes on cellular structure. Geometric morphometric methods, such as Procrustes superimposition, and Spherical Harmonics (SPHARM), have been used to capture the biological shape variation and group differences in morphology. Previous SPHARM-MAT techniques use the CALD algorithm to parameterize the mesh surface. It starts from initial mapping and performs local and global smoothing methods alternately to control the area and length distortions simultaneously. However, this parameterization may not be sufficient in complex morphological cases. To bridge this gap, we propose SPHARM-OT, an enhanced SPHARM surface modeling method using optimal transport (OT) for spherical parameterization. First, the genus 0 3D objects are conformally mapped onto a sphere. Then the optimal transport theory via spherical power diagram is introduced to minimize the area distortion. This new algorithm can effectively reduce the area distortion and lead to a better reconstruction result. We demonstrate the effectiveness of the method by applying it to the human sphenoidal paranasal sinuses.

Indexed as

paranasal sinusprincipal component analysisshape analysisSpherical harmonicsspherical optimal transport

Identifiers

PMID38013951
PMCPMC10676763

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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.