Active graph matching for automatic joint segmentation and annotation of C. elegans

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Item Type:	Article
Title:	Active graph matching for automatic joint segmentation and annotation of C. elegans
Creators Name:	Kainmueller, D., Jug, F., Rother, C. and Myers, G.
Abstract:	In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time.
Keywords:	Manual Segmentation, Graph Match, Active Shape Model, Nucleus Location, Active Graph, Animals, Caenorhabditis elegans
Source:	Lecture Notes in Computer Science
Series Name:	Lecture Notes in Computer Science
Title of Book:	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014
ISSN:	0302-9743
ISBN:	978-3-319-10403-4
Publisher:	Springer
Volume:	8673
Page Range:	81-88
Date:	2014
Official Publication:	https://doi.org/10.1007/978-3-319-10404-1_11
PubMed:	View item in PubMed

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