CoChat
Sign Up
Cited by 19

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Who Is Talking to Whom: Synaptic Partner Detection in Anisotropic Volumes of Insect Brain

Kreshuk, Anna, Funke, Jan, Cardona, Albert, Hamprecht, Fred A.·2015·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-24553-9_81
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1007/978-3-319-24553-9_81

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 1 references

Research works printed but not linked: 19

  1. Andres, B., Kroeger, T., Briggman, K.L., Denk, W., Korogod, N., Knott, G., Koethe, U., Hamprecht, F.A.: Globally Optimal Closed-surface Segmentation for Connectomics. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 778–791. Springer, Heidelberg (2012) · DOI
  2. Becker, C., Ali, K., Knott, G., Fua, P.: Learning Context Cues for Synapse Segmentation in EM Volumes. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 585–592. Springer, Heidelberg (2012) · DOI
  3. Funke, J., Andres, B., Hamprecht, F.A., Cardona, A., Cook, M.: Efficient automatic 3D-reconstruction of branching neurons from EM data. In: Proceedings of CVPR (2012) · DOI
  4. Funke, J., et al.: Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumes. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 17–24. Springer, Heidelberg (2014) · DOI
  5. Helmstaedter, M., Mitra, P.P.: Computational methods and challenges for large-scale circuit mapping. Current Opinion in Neurobiology 22(1), 162–169 (2012) · DOI
  6. Huang, G.B., Jain, V.: Deep and Wide Multiscale Recursive Networks for Robust Image Labeling. In: Proceedings of ICLR (2014)
  7. Jagadeesh, V., Anderson, J., Jones, B., Marc, R., Fisher, S., Manjunath, B.S.: Synapse Classification and Localization in Electron Micrographs. Pattern Recognition Letters (2013) · DOI
  8. Kaynig, V., Knowles-barley, S., Jones, T.R.: Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images. arxiv:1303.7186v1 (2013) · arXiv
  9. Knott, G., Marchman, H., Wall, D., Lich, B.: Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. The Journal of Neuroscience 28(12), 2959–2964 (2008) · DOI · Paper page
  10. Kreshuk, A., Köthe, U., Pax, E., Bock, D.D., Hamprecht, F.A.: Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks. PLoS One 9, 2 (2014) · DOI
  11. Kreshuk, A., Straehle, C.N., Sommer, C., Koethe, U., Cantoni, M., Knott, G., Hamprecht, F.A.: Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PloS One 6(10), e24899 (2011) · DOI
  12. Lichtman, J.W., Pfister, H., Shavit, N.: The big data challenges of connectomics. Nature Neuroscience 17(11), 1448–1454 (2014) · DOI
  13. Liu, T., Jones, C., Seyedhosseini, M., Tasdizen, T.: A modular hierarchical approach to 3D electron microscopy image segmentation. Journal of Neuroscience Methods 226, 88–102 (2014) · DOI
  14. Nunez-Iglesias, J., Kennedy, R., Parag, T., Shi, J., Chklovskii, D.B.: Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images. PloS One 8(8), e71715 (2013) · DOI
  15. Plaza, S.M.: Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images. arXiv:1409.1199v1 (2014) · arXiv
  16. Plaza, S.M., Parag, T., Huang, G.B., Olbris, D.J., Saunders, M.A., Rivlin, P.K.: Annotating Synapses in Large EM Datasets. arXiv:1409.1801v2 (2014) · arXiv
  17. Roncal, W.G., Kaynig-Fittkau, V., Kasthuri, N., Berger, D., Vogelstein, J.T., Fernandez, L.R., Lichtman, J.W., Vogelstein, R.J., Pfister, H., Hager, G.D.: Volumetric Exploitation of Synaptic Information using Context Localization and Evaluation. arXiv:1403.3724 (2014) · arXiv
  18. Sommer, C., Straehle, C., Kothe, U., Hamprecht, F.A.: Ilastik: Interactive learning and segmentation toolkit. In: Proceedings of ISBI (2011) · DOI
  19. Vazquez-Reina, A., Gelbart, M., Huang, D., Lichtman, J., Miller, E., Pfister, H.: Segmentation fusion for connectomics. In: Proceedings of ICCV (2011) · DOI

Cited by

Sorted by citations

Metadata

Authors
Kreshuk, A., Funke, J., Cardona, A., & Hamprecht, F. A.
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2015
Pages
661-668
ISSN
  • 0302-9743
  • 1611-3349
DOI
10.1007/978-3-319-24553-9_81
Cited by
19
References
1
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review