Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration Article Swipe
Toufiq Parag
,
Fabian Tschopp
,
William Grisaitis
,
Srinivas C. Turaga
,
Xuewen Zhang
,
Brian Matejek
,
Lee Kamentsky
,
Jeff W. Lichtman
,
Hanspeter Pfister
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1707.08935
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1707.08935
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent years, automatic dense reconstruction from anisotropic data remains a challenge for the connectomics community. One significant hurdle in the segmentation of anisotropic data is the difficulty in generating a suitable initial over-segmentation. In this study, we present a segmentation method for anisotropic EM data that agglomerates a 3D over-segmentation computed from the 3D affinity prediction. A 3D U-net is trained to predict 3D affinities by the MALIS approach. Experiments on multiple datasets demonstrates the strength and robustness of the proposed method for anisotropic EM segmentation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1707.08935
- https://arxiv.org/pdf/1707.08935
- OA Status
- green
- Cited By
- 11
- References
- 26
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2740188645
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2740188645Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1707.08935Digital Object Identifier
- Title
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Anisotropic EM Segmentation by 3D Affinity Learning and AgglomerationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-07-27Full publication date if available
- Authors
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Toufiq Parag, Fabian Tschopp, William Grisaitis, Srinivas C. Turaga, Xuewen Zhang, Brian Matejek, Lee Kamentsky, Jeff W. Lichtman, Hanspeter PfisterList of authors in order
- Landing page
-
https://arxiv.org/abs/1707.08935Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1707.08935Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1707.08935Direct OA link when available
- Concepts
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Connectomics, Segmentation, Anisotropy, Isotropy, Artificial intelligence, Robustness (evolution), Computer science, Artificial neural network, Pattern recognition (psychology), Connectome, Physics, Neuroscience, Optics, Chemistry, Biology, Gene, Functional connectivity, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1, 2020: 2, 2019: 6, 2018: 2Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| publication_date | 2017-07-27 |
| publication_year | 2017 |
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| referenced_works_count | 26 |
| abstract_inverted_index.A | 100 |
| abstract_inverted_index.a | 53, 73, 82, 91 |
| abstract_inverted_index.3D | 92, 97, 101, 107 |
| abstract_inverted_index.EM | 87, 128 |
| abstract_inverted_index.In | 35, 77 |
| abstract_inverted_index.by | 109 |
| abstract_inverted_index.in | 43, 62, 71 |
| abstract_inverted_index.is | 68, 103 |
| abstract_inverted_index.of | 2, 19, 37, 65, 122 |
| abstract_inverted_index.on | 41, 114 |
| abstract_inverted_index.or | 29 |
| abstract_inverted_index.to | 105 |
| abstract_inverted_index.we | 80 |
| abstract_inverted_index.One | 59 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 120 |
| abstract_inverted_index.for | 55, 85, 126 |
| abstract_inverted_index.has | 4 |
| abstract_inverted_index.the | 38, 56, 63, 69, 96, 110, 118, 123 |
| abstract_inverted_index.Most | 18 |
| abstract_inverted_index.data | 51, 67, 88 |
| abstract_inverted_index.from | 10, 15, 25, 49, 95 |
| abstract_inverted_index.near | 30 |
| abstract_inverted_index.that | 89 |
| abstract_inverted_index.this | 78 |
| abstract_inverted_index.were | 23 |
| abstract_inverted_index.MALIS | 111 |
| abstract_inverted_index.SBEM) | 33 |
| abstract_inverted_index.U-net | 102 |
| abstract_inverted_index.brain | 13 |
| abstract_inverted_index.data. | 34 |
| abstract_inverted_index.dense | 47 |
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| abstract_inverted_index.large | 12 |
| abstract_inverted_index.spite | 36 |
| abstract_inverted_index.these | 20 |
| abstract_inverted_index.(e.g., | 27, 32 |
| abstract_inverted_index.hurdle | 61 |
| abstract_inverted_index.method | 84, 125 |
| abstract_inverted_index.neural | 21 |
| abstract_inverted_index.neuron | 7 |
| abstract_inverted_index.recent | 44 |
| abstract_inverted_index.study, | 79 |
| abstract_inverted_index.wiring | 8 |
| abstract_inverted_index.years, | 45 |
| abstract_inverted_index.FIBSEM) | 28 |
| abstract_inverted_index.initial | 75 |
| abstract_inverted_index.predict | 106 |
| abstract_inverted_index.present | 81 |
| abstract_inverted_index.regions | 14 |
| abstract_inverted_index.remains | 52 |
| abstract_inverted_index.trained | 104 |
| abstract_inverted_index.affinity | 98 |
| abstract_inverted_index.animals. | 17 |
| abstract_inverted_index.computed | 24, 94 |
| abstract_inverted_index.datasets | 116 |
| abstract_inverted_index.diagrams | 9 |
| abstract_inverted_index.multiple | 16, 115 |
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| abstract_inverted_index.proposed | 124 |
| abstract_inverted_index.recently | 5 |
| abstract_inverted_index.strength | 119 |
| abstract_inverted_index.suitable | 74 |
| abstract_inverted_index.approach. | 112 |
| abstract_inverted_index.automatic | 46 |
| abstract_inverted_index.challenge | 54 |
| abstract_inverted_index.isotropic | 26, 31 |
| abstract_inverted_index.affinities | 108 |
| abstract_inverted_index.algorithms | 42 |
| abstract_inverted_index.community. | 58 |
| abstract_inverted_index.difficulty | 70 |
| abstract_inverted_index.generating | 72 |
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| abstract_inverted_index.remarkable | 39 |
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| abstract_inverted_index.anisotropic | 50, 66, 86, 127 |
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| abstract_inverted_index.significant | 60 |
| abstract_inverted_index.agglomerates | 90 |
| abstract_inverted_index.connectomics | 3, 57 |
| abstract_inverted_index.demonstrates | 117 |
| abstract_inverted_index.segmentation | 64, 83 |
| abstract_inverted_index.segmentation. | 129 |
| abstract_inverted_index.reconstruction | 48 |
| abstract_inverted_index.reconstructions | 22 |
| abstract_inverted_index.over-segmentation | 93 |
| abstract_inverted_index.over-segmentation. | 76 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |