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  Learning to segment neurons with non-local quality measures

Kroeger, T., Mikula, S., Denk, W., Koethe, U., & Hamprecht, F. A. (2013). Learning to segment neurons with non-local quality measures. In K. Mori, I. Sakuma, Y. Sato, C. Barillot, & N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013 (pp. 419-427). Berlin Heidelberg: Springer-Verlag Berlin Heidelberg.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-C797-5 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-C798-3
Genre: Book Chapter

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MedImageComputComputAssistInter_16_2013_419.pdf (Any fulltext), 1015KB
 
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http://link.springer.com/content/pdf/10.1007%2F978-3-642-40763-5_52.pdf (Any fulltext)
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 Creators:
Kroeger, Thorben, Author
Mikula, Shawn1, Author           
Denk, Winfried1, Author           
Koethe, Ullrich, Author
Hamprecht, Fred A., Author
Affiliations:
1Department of Biomedical Optics, Max Planck Institute for Medical Research, Max Planck Society, ou_1497699              

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 Abstract: Segmentation schemes such as hierarchical region merging or correllation clustering rely on edge weights between adjacent (super-)voxels. The quality of these edge weights directly affects the quality of the resulting segmentations. Unstructured learning methods seek to minimize the classification error on individual edges. This ignores that a few local mistakes (tiny boundary gaps) can cause catastrophic global segmentation errors. Boundary evidence learning should therefore optimize structured quality criteria such as Rand Error or Variation of Information. We present the first structured learning scheme using a structured loss function; and we introduce a new hierarchical scheme that allows to approximately solve the NP hard prediction problem even for huge volume images. The value of these contributions is demonstrated on two challenging neural circuit reconstruction problems in serial sectioning electron microscopic images with billions of voxels. Our contributions lead to a partitioning quality that improves over the current state of the art

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Language(s): eng - English
 Dates: 2013
 Publication Status: Issued
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: Other: 8056
DOI: 10.1007/978-3-642-40763-5_52
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Title: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Source Genre: Book
 Creator(s):
Mori, Kensaku, Editor
Sakuma, Ichiro, Editor
Sato, Yoshinobu, Editor
Barillot, Christian, Editor
Navab, Nassir, Editor
Affiliations:
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Publ. Info: Berlin Heidelberg : Springer-Verlag Berlin Heidelberg
Pages: - Volume / Issue: 8150 Sequence Number: - Start / End Page: 419 - 427 Identifier: ISBN: 978-3-642-40762-8
ISBN: 978-3-642-40763-5