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Efficient automatic 3D-reconstruction of branching neurons from EM data


Funke, Jan; Andres, Bjoern; Hamprecht, Fred A; Cardona, Albert; Cook, Matthew (2012). Efficient automatic 3D-reconstruction of branching neurons from EM data. In: Conference on Computer Vision and Pattern Recognition (CVPR) , Providence, Rhode Island, USA, 16 June 2012 - 21 June 2012, 1004 - 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.

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.

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17 citations in Web of Science®
16 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:21 June 2012
Deposited On:28 Feb 2013 07:41
Last Modified:05 Apr 2016 16:36
Publisher:I E E E Computer Society
Series Name:IEEE Conference on Computer Vision and Pattern Recognition. Proceedings
Number of Pages:8
ISSN:1063-6919
ISBN:978-1-4673-1227-1
Additional Information:© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher DOI:https://doi.org/10.1109/CVPR.2012.6247777
Permanent URL: https://doi.org/10.5167/uzh-75312

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