IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007012.html
   My bibliography  Save this article

DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images

Author

Listed:
  • Victor Kulikov
  • Syuan-Ming Guo
  • Matthew Stone
  • Allen Goodman
  • Anne Carpenter
  • Mark Bathe
  • Victor Lempitsky

Abstract

Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet.Author summary: Multiplexed fluorescence imaging of synaptic proteins facilitates high throughput investigations in neuroscience and drug discovery. Currently, there are several approaches to synapse detection using computational image processing. Unsupervised techniques rely on the a priori knowledge of synapse properties, such as size, intensity, and co-localization of synapse markers in each channel. For each experimental replicate, these parameters are typically tuned manually in order to obtain appropriate results. In contrast, supervised methods like modern convolutional networks require massive amounts of manually labeled data, and are sensitive to signal/noise ratios. As an alternative, here we propose DoGNet, a neural architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. This approach leverages the strengths of each approach, including automatic tuning of detection parameters, prior knowledge of the synaptic signal shape, and requiring only several training examples. Overall, DoGNet is a new tool for blob detection from multiplexed fluorescence images consisting of several up to dozens of fluorescence channels that requires minimal supervision due to its few input parameters. It offers the ability to capture complex dependencies between synaptic signals in distinct imaging planes, acting as a trainable frequency filter.

Suggested Citation

  • Victor Kulikov & Syuan-Ming Guo & Matthew Stone & Allen Goodman & Anne Carpenter & Mark Bathe & Victor Lempitsky, 2019. "DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-20, May.
  • Handle: RePEc:plo:pcbi00:1007012
    DOI: 10.1371/journal.pcbi.1007012
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007012
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007012&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1007012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Anish K Simhal & Cecilia Aguerrebere & Forrest Collman & Joshua T Vogelstein & Kristina D Micheva & Richard J Weinberg & Stephen J Smith & Guillermo Sapiro, 2017. "Probabilistic fluorescence-based synapse detection," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-24, April.
    2. Anna Kreshuk & Christoph N Straehle & Christoph Sommer & Ullrich Koethe & Marco Cantoni & Graham Knott & Fred A Hamprecht, 2011. "Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-8, October.
    3. João Peça & Cátia Feliciano & Jonathan T. Ting & Wenting Wang & Michael F. Wells & Talaignair N. Venkatraman & Christopher D. Lascola & Zhanyan Fu & Guoping Feng, 2011. "Shank3 mutant mice display autistic-like behaviours and striatal dysfunction," Nature, Nature, vol. 472(7344), pages 437-442, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juan Nunez-Iglesias & Ryan Kennedy & Toufiq Parag & Jianbo Shi & Dmitri B Chklovskii, 2013. "Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-11, August.
    2. Tetsushi Sadakata & Yo Shinoda & Akira Sato & Hirotoshi Iguchi & Chiaki Ishii & Makoto Matsuo & Ryosuke Yamaga & Teiichi Furuichi, 2013. "Mouse Models of Mutations and Variations in Autism Spectrum Disorder-Associated Genes: Mice Expressing Caps2/Cadps2 Copy Number and Alternative Splicing Variants," IJERPH, MDPI, vol. 10(12), pages 1-19, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.