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  Convolutional networks can learn to generate affinity graphs for image segmentation

Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., et al. (2010). Convolutional networks can learn to generate affinity graphs for image segmentation. Neural computation, 22(2), 511-538. doi:10.1162/neco.2009.10-08-881.

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 Creators:
Turaga, Srinivas C., Author
Murray, Joseph F., Author
Jain, Viren, Author
Roth, Fabian, Author
Helmstaedter, Moritz1, 2, Author           
Briggman, Kevin1, Author           
Denk, Winfried1, Author           
Seung, H. Sebastian, Author
Affiliations:
1Department of Biomedical Optics, Max Planck Institute for Medical Research, Max Planck Society, ou_1497699              
2Department of Cell Physiology, Max Planck Institute for Medical Research, Max Planck Society, ou_1497701              

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 Abstract: Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand−designed affinity functions.

We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms.

In contrast to previous work, we do not rely on prior knowledge in the form of hand−designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types

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Language(s): eng - English
 Dates: 2008-09-102009-05-142010-02-01
 Publication Status: Issued
 Pages: 29
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 Rev. Type: Peer
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 22 (2) Sequence Number: - Start / End Page: 511 - 538 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591