DocumentCode :
2713834
Title :
Efficient automatic 3D-reconstruction of branching neurons from EM data
Author :
Funke, Jan ; Andres, Bjoern ; Hamprecht, Fred A. ; Cardona, Albert ; Cook, Matthew
fYear :
2012
fDate :
16-21 June 2012
Firstpage :
1004
Lastpage :
1011
Abstract :
We present an approach for the automatic reconstruction of neurons from 3D stacks of electron microscopy sections. The core of our system is a set of possible assignments, each of which proposes with some cost a link between neuron regions in consecutive sections. These can model the continuation, branching, and end of neurons. The costs are trainable on positive assignment samples. An optimal and consistent set of assignments is found for the whole volume at once by solving an integer linear program. This set of assignments determines both the segmentation into neuron regions and the correspondence between such regions in neighboring slices. For each picked assignment, a confidence value helps to prioritize decisions to be reviewed by a human expert. We evaluate the performance of our method on an annotated volume of neural tissue and compare to the current state of the art [26]. Our method is superior in accuracy and can be trained using a small number of samples. The observed inference times are linear with about 2 milliseconds per neuron and section.
Keywords :
biological tissues; brain; electron microscopy; image reconstruction; image segmentation; integer programming; medical image processing; 3D stacks; EM data; automatic 3D-reconstruction; automatic neuron reconstruction; branching neurons; electron microscopy sections; integer linear program; neural tissue; neuron regions; Image reconstruction; Image segmentation; Neurons; Training; Vectors; Vegetation; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location :
Providence, RI
ISSN :
1063-6919
Print_ISBN :
978-1-4673-1226-4
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2012.6247777
Filename :
6247777
Link To Document :
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