Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation
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Seung_Convolutional Networks.pdf
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1.52 MB
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Checksum (MD5)
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Author(s)
Turaga, Srinivas C.
Murray, Joseph F.
Jain, Viren
Roth, Fabian
Helmstaedter, Moritz N.
Briggman, Kevin L.
Denk, Winfried
Seung, H. Sebastian
Date Issued
January 2010
Journal
Neural Computation
Publisher
MIT Press
Citation
Turaga, Srinivas C. et al. “Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation.” Neural Computation 22.2 (2011): 511-538. © 2009 Massachusetts Institute of Technology
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1162/neco.2009.10-08-881
