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High-accuracy neurite reconstruction for high-throughput neuroanatomy

Nature Neuroscience volume 14, pages 1081–1088 (2011)Cite this article

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Abstract

Neuroanatomic analysis depends on the reconstruction of complete cell shapes. High-throughput reconstruction of neural circuits, or connectomics, using volume electron microscopy requires dense staining of all cells, which leads even experts to make annotation errors. Currently, reconstruction speed rather than acquisition speed limits the determination of neural wiring diagrams. We developed a method for fast and reliable reconstruction of densely labeled data sets. Our approach, based on manually skeletonizing each neurite redundantly (multiple times) with a visualization-annotation software tool called KNOSSOS, is ∼50-fold faster than volume labeling. Errors are detected and eliminated by a redundant-skeleton consensus procedure (RESCOP), which uses a statistical model of how true neurite connectivity is transformed into annotation decisions. RESCOP also estimates the reliability of consensus skeletons. Focused reannotation of difficult locations promises a rather steep increase of reliability as a function of the average skeleton redundancy and thus the nearly error-free analysis of large neuroanatomical datasets.

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Figure 1: Comparison of volume and skeleton annotation.
Figure 2: Skeletonization by expert annotators.
Figure 3: RESCOP step 1, skeleton-to-skeleton agreement measurement.
Figure 4: RESCOP steps 2 and 3, edge elimination and skeleton recombination.
Figure 5: RESCOP step 4, estimating error rate of RESCOPed skeletons.
Figure 6: Doubly annotated skeletons of 114 putative rod bipolar cells in a block of mouse retina.

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Acknowledgements

We thank B. Andres, F. Hamprecht, U. Köthe, V. Jain, S. Seung and S. Turaga for many fruitful discussions and comments on the manuscript, J. Bollmann and A. Schaefer for helpful comments on the manuscript, C. Roome for information technology support, J. Kornfeld and F. Svara for programming KNOSSOS, and J. Hanne, H. Jakobi and H. Wissler for help with annotator training. We thank N. Abazova, E. Abs, A. Antunes, P. Bastians, J. Bauer, M. Beez, M. Beining, S. Bender, S. Best, L. Brosi, M. Bucher, E. Buckler, J. Buhmann, C. Burkhardt, F. Drawitsch, L. Ehm, S. Ehm, C. Fianke, R. Foltin, S. Freiss, M. Funk, A. Gebhardt, M. Gruen, K. Haase, J. Hammerich, J. Hanne, B. Hauber, M. Hensen, L. Hofmann, P. Hofmann, M. Hülser, F. Isensee, H. Jakobi, M. Jonczyk, A. Joschko, A. Juenger, S. Kaspar, K. Kessler, A. Khan, M. Kiapes, A. Klein, C. Klein, S. Laiouar, T. Lang, L. Lebelt, H. Lesch, C. Lieven, D. Luft, E. Moeller, A. Muellner, M. Mueller, D. Ollech, A. Oppold, T. Otolski, S. Oumohand, S. Pfarr, M. Pohrath, A. Poos, S. Putzke, J. Reinhardt, A. Rommerskirchen, M. Roth, J. Sambel, K. Schramm, C. Sellmann, J. Sieber, I. Sonntag, M. Stahlberg, T. Stratmann, J. Trendel, F. Trogisch, M. Uhrig, A. Vogel, J. Volz, C. Weber, P. Weber, K. Weiss, L. Weisshaar, E. Wiegand, T. Wiegand, M. Wiese, R. Wiggers, C. Willburger and A. Zegarra for neurite skeletonizations. This work was funded by the Max Planck Society.

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Authors and Affiliations

  1. Max Planck Institute for Medical Research, Heidelberg, Germany

    Moritz Helmstaedter, Kevin L Briggman & Winfried Denk

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  1. Moritz Helmstaedter
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  2. Kevin L Briggman
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Contributions

M.H. and W.D. designed the study and devised the analysis algorithms; K.L.B. carried out the SBEM experiments; M.H., K.L.B. and W.D. specified the KNOSSOS software; M.H. analyzed the data; M.H., K.L.B. and W.D. wrote the paper.

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Correspondence to Moritz Helmstaedter.

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Competing interests

Moritz Helmstaedter and Winfried Denk have applied for a patent (Published Patent Application US 20100183217). Winfried Denk receives IP license income from Gatan Inc. for serial blockface imaging.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 (PDF 576 kb)

Supplementary Movie 1

Skeleton tracing with KNOSSOS. 3 orthogonal views are displayed (xy, top left, yz, top right, xz, bottom left) and a 3-dimensional view of the dataset bounding box and the skeleton being created (bottom right). The 19MB version requires a video player compatible with DivX-encoded files. The 68MB version can be viewed with a generic player but is shorter. (ZIP 51786 kb)

Supplementary Image Stack 1 (ZIP 2890 kb)

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Helmstaedter, M., Briggman, K. & Denk, W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 14, 1081–1088 (2011). https://doi.org/10.1038/nn.2868

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