| |  Online-Ressource |
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| Verfasst von: | Krasowski, Nikola Enrico [VerfasserIn]  |
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| | Beier, Thorsten [VerfasserIn] |
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| | Köthe, Ullrich [VerfasserIn] |
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| | Hamprecht, Fred [VerfasserIn] |
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| | Kreshuk, Anna [VerfasserIn] |
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| Titel: | Neuron segmentation with high-level biological priors |
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| Verf.angabe: | N. E. Krasowski, T. Beier, G. W. Knott, U. Köthe, F. A. Hamprecht, and A. Kreshuk |
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| Jahr: | 2018 |
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| Jahr des Originals: | 2017 |
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| Umfang: | 11 S. |
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| Fussnoten: | Publication: 06 June 2017 ; Gesehen am 28.10.2019 |
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| Titel Quelle: | Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on medical imaging |
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| Ort Quelle: | New York, NY : Institute of Electrical and Electronics Engineers, 1982 |
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| Jahr Quelle: | 2018 |
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| Band/Heft Quelle: | 37(2018), 4, Seite 829-839 |
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| ISSN Quelle: | 1558-254X |
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| Abstract: | We present a novel approach to the problem of neuron segmentation in image volumes acquired by an electron microscopy. Existing methods, such as agglomerative or correlation clustering, rely solely on boundary evidence and have problems where such an evidence is lacking (e.g., incomplete staining) or ambiguous (e.g., co-located cell and mitochondria membranes). We investigate if these difficulties can be overcome by means of sparse region appearance cues that differentiate between pre- and postsynaptic neuron segments in mammalian neural tissue. We combine these cues with the traditional boundary evidence in the asymmetric multiway cut (AMWC) model, which simultaneously solves the partitioning and the semantic region labeling problems. We show that AMWC problems over superpixel graphs can be solved to global optimality with a cutting plane approach, and that the introduction of semantic class priors leads to significantly better segmentations. |
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| DOI: | doi:10.1109/TMI.2017.2712360 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: http://dx.doi.org/10.1109/TMI.2017.2712360 |
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| | DOI: https://doi.org/10.1109/TMI.2017.2712360 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | agglomerative correlation clustering |
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| | Algorithms |
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| | AMWC problems |
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| | Animals |
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| | asymmetric multiway cut model |
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| | automated tracing |
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| | biological tissues |
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| | Biomembranes |
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| | co-located cell |
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| | connectomics |
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| | cutting plane approach |
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| | electron microscopy |
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| | graph theory |
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| | high-level biological priors |
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| | Image edge detection |
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| | Image Processing, Computer-Assisted |
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| | image resolution |
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| | image segmentation |
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| | Image segmentation |
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| | image volumes |
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| | incomplete staining |
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| | Labeling |
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| | mammalian neural tissue |
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| | medical image processing |
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| | Mice |
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| | Microscopy, Electron |
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| | mitochondria membranes |
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| | neuron segmentation |
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| | Neurons |
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| | pattern clustering |
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| | postsynaptic neuron segments |
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| | probabilistic graphical model |
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| | Segmentation |
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| | semantic class priors |
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| | semantic region labeling problems |
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| | Semantics |
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| | sparse region appearance cues |
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| K10plus-PPN: | 1680043676 |
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| Verknüpfungen: | → Zeitschrift |
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Neuron segmentation with high-level biological priors / Krasowski, Nikola Enrico [VerfasserIn]; 2018 (Online-Ressource)
