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Title: Quantification and Visualization of Variation in Anatomical Trees

Abstract

This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.

Authors:
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Publication Date:
Research Org.:
Nevada Test Site/National Security Technologies, LLC (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1179459
Report Number(s):
DOE/NV/25946-2016
Journal ID: 978-3-319-16347-5
DOE Contract Number:  
DE-AC52-06NA25946
Resource Type:
Conference
Journal Name:
Proceedings from the Women in Shape Modeling Workshop, Institute for Pure and Applied Mathematics
Additional Journal Information:
Conference: Women in Shape Modeling Workshop, Institute for Pure and Applied Mathematics, July 2014
Country of Publication:
United States
Language:
English
Subject:
dataset, tree, quantification, visualization

Citation Formats

Amenta, Nina, Datar, Manasi, Dirksen, Asger, de Bruihne, Marleen, Feragen, Aasa, Ge, Xiaoyin, Holst Pedersen, Jesper, Howard, Marylesa, Owen, Megan, Petersen, Jens, Shi, Jie, and Xu, Qiuping. Quantification and Visualization of Variation in Anatomical Trees. United States: N. p., 2015. Web.
Amenta, Nina, Datar, Manasi, Dirksen, Asger, de Bruihne, Marleen, Feragen, Aasa, Ge, Xiaoyin, Holst Pedersen, Jesper, Howard, Marylesa, Owen, Megan, Petersen, Jens, Shi, Jie, & Xu, Qiuping. Quantification and Visualization of Variation in Anatomical Trees. United States.
Amenta, Nina, Datar, Manasi, Dirksen, Asger, de Bruihne, Marleen, Feragen, Aasa, Ge, Xiaoyin, Holst Pedersen, Jesper, Howard, Marylesa, Owen, Megan, Petersen, Jens, Shi, Jie, and Xu, Qiuping. Wed . "Quantification and Visualization of Variation in Anatomical Trees". United States. https://www.osti.gov/servlets/purl/1179459.
@article{osti_1179459,
title = {Quantification and Visualization of Variation in Anatomical Trees},
author = {Amenta, Nina and Datar, Manasi and Dirksen, Asger and de Bruihne, Marleen and Feragen, Aasa and Ge, Xiaoyin and Holst Pedersen, Jesper and Howard, Marylesa and Owen, Megan and Petersen, Jens and Shi, Jie and Xu, Qiuping},
abstractNote = {This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.},
doi = {},
journal = {Proceedings from the Women in Shape Modeling Workshop, Institute for Pure and Applied Mathematics},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {7}
}

Conference:
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