Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Aasa Feragen , Jens Petersen , Dominik Grimm , Asger Dirksen , Jesper Holst Pedersen , Karsten Borgwardt , Marleen de Bruijne

ABSTRACT

Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree. While state-of-the-art graph and tree kernels use combinatorial tree/graph structure with discrete node and edge labels, the kernels presented in this paper can include geometric information such as branch shape, branch radius or other vector valued properties. In addition to being flexible in their ability to model different types of attributes, the presented kernels are computationally efficient and some of them can easily be computed for large datasets (N ~10.000) of trees with 30 − 600 branches. Combining the kernels with standard machine learning tools enables us to analyze the relation between disease and anatomical tree structure and geometry. Experimental results: The kernels are used to compare airway trees segmented from low-dose CT, endowed with branch shape descriptors and airway wall area percentage measurements made along the tree. Using kernelized hypothesis testing we show that the geometric airway trees are significantly differently distributed in patients with Chronic Obstructive Pulmonary Disease (COPD) than in healthy individuals. The geometric tree kernels also give a significant increase in the classification accuracy of COPD from geometric tree structure endowed with airway wall thickness measurements in comparison with state-of-the-art methods, giving further insight into the relationship between airway wall thickness and COPD. Software: Software for computing kernels and statistical tests is available at http://image.diku.dk/aasa/software.php. More... »

PAGES

171-183

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-38868-2_15

DOI

http://dx.doi.org/10.1007/978-3-642-38868-2_15

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https://app.dimensions.ai/details/publication/pub.1029559471

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24683967


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