IRUS Total

Learning and combining image similarities for neonatal brain population studies

File Description SizeFormat 
zimmer2015mlmi.pdfAccepted version358.04 kBUnknownView/Open
Title: Learning and combining image similarities for neonatal brain population studies
Authors: Zimmer, V
Glocker, B
Aljabar, P
Counsell, S
Rutherford, M
Edwards, AD
Hajnal, J
Gonzales Ballester, MA
Rueckert, D
Piella, G
Item Type: Conference Paper
Abstract: © Springer International Publishing Switzerland 2015.The characterization of neurodevelopment is challenging due to the complex structural changes of the brain in early childhood. To analyze the changes in a population across time and to relate them with clinical information, manifold learning techniques can be applied. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure in the embedding and highly application dependent. It has been shown that the combination of several notions of similarity and features can improve the new representation. However, how to combine and weight different similarites and features is non-trivial. In this work, we propose to learn the neighborhood structure and similarity measure used for manifold learning through Neighborhood Approximation Forests (NAFs). The recently proposed NAFs learn a neighborhood structure in a dataset based on a user-defined distance. A characterization of image similarity using NAFs enables us to construct manifold representations based on a previously defined criterion to improve predictions regarding structural and clinical information. In particular, NAFs can be used naturally to combine the affinities learned from multiple distances in a joint manifold towards a more meaningful representation and an improved characterization of the resulting embedding. We demonstrate the utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information.
Issue Date: 2-Oct-2015
Date of Acceptance: 1-Aug-2015
URI: http://hdl.handle.net/10044/1/26578
DOI: http://dx.doi.org/10.1007/978-3-319-24888-2_14
ISBN: 978-3-319-24887-5
ISSN: 0302-9743
Publisher: Springer International Publishing
Start Page: 110
End Page: 117
Journal / Book Title: Lecture Notes in Computer Science
Volume: 9352
Copyright Statement: © 2015 Springer International Publishing Switzerland. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24888-2_14
Conference Name: International Workshop on Machine Learning in Medical Imaging (MLMI)
Publication Status: Published
Start Date: 2015-10-05
Conference Place: Munich, Germany
Appears in Collections:Computing
Department of Medicine (up to 2019)
Faculty of Engineering