Learning and combining image similarities for neonatal brain population studies

TY - CHAP

T1 - Learning and combining image similarities for neonatal brain population studies

AU - Zimmer, Veronika A.

AU - Glocker, Ben

AU - Aljabar, Paul

AU - Counsell, Serena

AU - Rutherford, Mary

AU - Edwards, A. David

AU - Hajnal, Jo V.

AU - Ballester, Miguel Ángel González

AU - Rueckert, Daniel

AU - Piella, Gemma

PY - 2015/10/2

Y1 - 2015/10/2

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84952022658&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-24888-2_14

DO - 10.1007/978-3-319-24888-2_14

M3 - Conference paper

AN - SCOPUS:84952022658

SN - 9783319248875

VL - 9352

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 110

EP - 117

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer-Verlag Berlin Heidelberg

T2 - 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015

Y2 - 5 October 2015 through 5 October 2015

ER -