Abstract
In biological imaging the data is often represented by a sequence of anisotropic frames — the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present the details and additional evaluation of an approach originally introduced in Laptev et al. IEEE 11th International Symposium on Biomedical Imaging ISBI 2014. IEEE Xplore (2014) called SuperSlicing to decompose the observed frame into a sequence of plausible hidden sub-frames. Based on sub-frame decomposition by SuperSlicing we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular connectomics benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms. We also generalize the approach for video anisotropicity that comes from the long exposure time and show that our method outperforms baseline methods on a reconstruction of low frame rate videos of natural scenes.
Editors: Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Javier Orlandi, Bisakha Ray, Jordi Soriano
The original form of this article appears in JMLR W&CP Volume 46.
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- 1.
Video is called anisotropic or full-exposure if exposure time equals to the time between two frames.
- 2.
We do not compare with Shahar et al. (2011) directly, as their method operates under different assumptions and, moreover, they provide no quantitative results.
References
S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and R. Szeliski. A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92:1–31, 2011.
L. Breiman. Random forests. Machine Learning, 45(1):5–32, 2001.
A. Cardona and S. Saalfeld et al. An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol, 10, 2010.
T. Hu and J. Nunez-Iglesias et al. Super-resolution using sparse representations over learned dictionaries: Reconstruction of brain structure using electron microscopy. CoRR, abs/1210.0564, 2012.
V. Jain and B. Bollmann et al. Boundary learning by optimization with topological constraints. In CVPR, pages 2488–2495, 2010.
V. Kaynig, T. J. Fuchs, and J. M. Buhmann. Geometrical consistent 3d tracing of neuronal processes in sstem data. In MICCAI 2010, pages 209–216. Springer Berlin / Heidelberg, 2010.
D. Laptev, A. Vezhnevets, S. Dwivedi, and J. M. Buhmann. Anisotropic sstem image segmentation using dense correspondence across sections. In MICCAI, pages 323–330, 2012.
D. Laptev, A. Vezhnevets, and J. M. Buhmann. Superslicing frame restoration for anisotropic sstem. In IEEE 11th International Symposium on Biomedical Imaging ISBI 2014. IEEE Xplore, 2014.
D. G. Lowe. Object recognition from local scale-invariant features. In ICCV, pages 1150–. IEEE, 1999.
K. Sandberg and M. Brega. Segmentation of thin structures in electron micrographs using orientation fields. Journal of Structural Biology, 157(2):403–415, 2007.
Christian Schuldt, Ivan Laptev, and Barbara Caputo. Recognizing human actions: A local svm approach. In ICPR, 2004.
S. Seung. Connectome: How the brain’s wiring makes us who we are. Houghton Mifflin Harcourt, 2012.
Oded Shahar, Alon Faktor, and Michal Irani. Space-time super-resolution from a single video. In CVPR, pages 3353–3360, 2011.
M. Shimano, T. Okabe, I. Sato, and Y. Sato. Video temporal super-resolution based on self-similarity. In ACCV, pages 93–106, 2010.
Acknowledgements
This work was partially supported by the SNF grant Sinergia CRSII3_130470/1.
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Laptev, D., Buhmann, J.M. (2017). SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data. In: Battaglia, D., Guyon, I., Lemaire, V., Orlandi, J., Ray, B., Soriano, J. (eds) Neural Connectomics Challenge. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-53070-3_9
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DOI: https://doi.org/10.1007/978-3-319-53070-3_9
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