Fetching the paper…
Reading the bibliography…
Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.
W. M. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association
1971
Earlier work this paper cites.
J. G. White, E. Southgate, J. N. Thomson, and S. Brenner. The structure of the nervous system of the nematode Ceanorhabditis elegans
1986
Earlier work this paper cites.
R. Unnikrishnan, C. Pantofaru, and M. Hebert. Toward objective evaluation of image segmentation algorithms. Pattern Analysis and Machine Intelligence, IEEE Transactions on
2007
Earlier work this paper cites.
Z. Tu. Auto-context and its application to high-level vision tasks. In CVPR
2008
Earlier work this paper cites.
S. C. Turaga, K. Briggman, M. Helmstaedter, W. Denk, and H. S. Seung. Maximin affinity learning of image segmentation. In NIPS
2009
Earlier work this paper cites.
V. Jain, H. S. Seung, and S. C. Turaga. Machines that learn to segment images: a crucial technology for connectomics. Current Opinion in Neurobiology
2010
Earlier work this paper cites.
E. Jurrus, A. R. C. Paiva, S. Watanabe, J. R. Anderson, B. W. Jones, R. T. Whitaker, E. M. Jorgensen, R. E. Marc, and T. Tasdizen. Detection of neuron membranes in electron microscopy images using a serial neural network architecture. Medical Image Analysis
2010
Earlier work this paper cites.
S. C. Turaga, J. F. Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman, W. Denk, and H. S. Seung. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Computation
2010
Earlier work this paper cites.
K. L. Briggman and D. D. Bock. Volume electron microscopy for neuronal circuit reconstruction. Current Opinion in Neurobiology
2012
Earlier work this paper cites.
Segmentation of neuronal structures in EM stacks challenge - ISBI 2012. http://brainiac2.mit.edu/isbi_challenge/
2012
Earlier work this paper cites.
D. C. Cireşan, A. Giusti, L. M. Gambardella, and J. Schmidhuber. Deep neural networks segment neuronal membranes in electron microscopy images. In NIPS
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS
2012
Earlier work this paper cites.
J. C. Tapia, N. Kasthuri, K. J. Hayworth, R. Schalek, J. W. Lichtman, S. J. Smith, and J. Buchanan. High-contrast en bloc staining of neuronal tissue for field emission scanning electron microscopy. Nature Protocols
2012
Earlier work this paper cites.
S. Takemura et al. A visual motion detection circuit suggested by Drosophila
2013
Cited alongside, same era.
M. Helmstaedter, K. L. Briggman, S. C. Turaga, V. Jain, H. S. Seung, and W. Denk. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature
2013
Cited alongside, same era.
M. Helmstaedter. Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nature Methods
2013
Cited alongside, same era.
M. Seyedhosseini and T. Tasdizen. Multi-class multi-scale series contextual model for image segmentation. Image Processing, IEEE Transactions on
2013
Cited alongside, same era.
A. Giusti, D. C. Cireşan, J. Masci, L. M. Gambardella, and J. Schmidhuber. Fast image scanning with deep max-pooling convolutional neural networks. In ICIP
2013
Cited alongside, same era.
M. Mathieu, M. Henaff, and Y. LeCun. Fast training of convolutional networks through FFTs. In ICLR
2014
Later among the works it cites.
K. J. Hayworth, J. L. Morgan, R. Schalek, D. R. Berger, D. G. Hildebrand, and J. W. Lichtman. Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits. Frontiers in Neural Circuits
2014
Later among the works it cites.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. OverFeat: Integrated recognition, localization and detection using convolutional networks. In ICLR
2014
Later among the works it cites.
M. Chen, Y. Yan, X. Gong, C. D. Gilbert, H. Liang, and W. Li. Incremental integration of global contours through interplay between visual cortical areas. Neuron
2014
Later among the works it cites.
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Masci, A. Giusti, D. C. Cireşan, G. Fricout, and J. Schmidhuber. A fast learning algorithm for image segmentation with max-pooling convolutional networks. In ICIP
2013
Cited alongside, same era.
J. S. Kim, M. J. Greene, A. Zlateski, K. Lee, M. Richardson, S. C. Turaga, M. Purcaro, M. Balkam, A. Robinson, B. F. Behabadi, M. Campos, W. Denk, H. S. Seung, and the EyeWirers. Space-time wiring specificity supports direction selectivity in the retina. Nature
2014
Cited alongside, same era.
T. Tasdizen, S. M. Seyedhosseini, T. Liu, C. Jones, and E. Jurrus. Image segmentation for connectomics using machine learning. In Computational Intelligence in Biomedical Imaging
2014
Cited alongside, same era.
T. Liu, C. Jones, M. Seyedhosseini, and T. Tasdizen. A modular hierarchical approach to 3D electron microscopy image segmentation. Journal of Neuroscience Methods
2014
Cited alongside, same era.
G. B. Huang and V. Jain. Deep and wide multiscale recursive networks for robust image labeling. In ICLR
2014
Cited alongside, same era.
2014
Cited alongside, same era.
P. O. Pinheiro and R. Collobert. Recurrent convolutional neural networks for scene labeling. In ICML
2014
Cited alongside, same era.
2015
Closest in time.
2015
Closest in time.
2015
Closest in time.
N. Vasilache, J. Johnson, M. Mathieu, S. Chintala, S. Piantino, and Y. LeCun. Fast convolutional nets with fbfft: a GPU performance evaluation. In ICLR
2015
Closest in time.
Kasthuri N, Hayworth K, et al. Saturated reconstruction of a volume of neocortex. Cell
2015
Closest in time.
2015
Closest in time.
J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR
2015
Closest in time.
Y. LeCun, B. Yoshu, and G. Hinton. Deep learning. Nature
2015
Closest in time.