Deep contextual networks for neuronal structure segmentation

30th AAAI Conference on Artificial Intelligence, AAAI 2016

Volumn , Issue , 2016, Pages 1167-1173

  Chen, Hao ^a   Qi, Xiaojuan ^a   Cheng, Jie Zhi ^b   Heng, Pheng Ann ^a,c  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b Shenzhen University School of Medicine   (China)

^c SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; NEURAL NETWORKS; NEURONS; SEMANTICS;

BENCHMARK DATASETS; CONTEXTUAL INFORMATION; DEEP NEURAL NETWORKS; HIERARCHICAL STRUCTURES; NEURONAL STRUCTURE; SEGMENTATION PERFORMANCE; STATE-OF-THE-ART METHODS; STRUCTURAL BOUNDARY;

IMAGE SEGMENTATION;

EID: 85007271408     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Bengio, Y.; Lamblin, P.; Popovici, D.; Larochelle, H.; et al. 2007. Greedy layer-wise training of deep networks. Advances in neural information processing systems 19:153.
2. Beucher, S., and Lantuejoul, C. 1979. Use of watersheds in contour detection. In International Conference on Image Processing.
3. Bock, D. D.; Lee, W.-C. A.; Kerlin, A. M.; Andermann, M. L.; Hood, G.;Wetzel, A.W.; Yurgenson, S.; Soucy, E. R.; Kim, H. S.; and Reid, R. C. 2011. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471(7337):177-182.
4. Cardona, A.; Saalfeld, S.; Preibisch, S.; Schmid, B.; Cheng, A.; Pulokas, J.; Tomancak, P.; and Hartenstein, V. 2010. An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS biology 8(10):2564.
5. Cardona, A.; Saalfeld, S.; Schindelin, J.; Arganda-Carreras, I.; Preibisch, S.; Longair, M.; Tomancak, P.; Hartenstein, V.; and Douglas, R. J. 2012. Trakem2 software for neural circuit reconstruction. PloS one 7(6):e38011.
6. Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2014. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062.
7. Chen, H.; Shen, C.; Qin, J.; Ni, D.; Shi, L.; Cheng, J. C.; and Heng, P.-A. 2015. Automatic localization and identification of vertebrae in spine ct via a joint learning model with deep neural networks. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Springer. 515-522.
8. Ciresan, D.; Giusti, A.; Gambardella, L. M.; and Schmidhuber, J. 2012. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in neural information processing systems, 2843-2851.
9. Ignacio, A.-C.; Sebastian, S.; Albert, C.; and Johannes, S. 2012. 2012 ISBI Challenge: Segmentation of neuronal structures in EM stacks. http://brainiac2.mit.edu/isbichallenge/.
10. Jain, V.; Bollmann, B.; Richardson, M.; Berger, D. R.; Helmstaedter, M. N.; Briggman, K. L.; Denk, W.; Bowden, J. B.; Mendenhall, J. M.; Abraham, W. C.; et al. 2010. Boundary learning by optimization with topological constraints. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 2488-2495. IEEE.
11. Jia, Y.; Shelhamer, E.; Donahue, J.; Karayev, S.; Long, J.; Girshick, R.; Guadarrama, S.; and Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093.
12. Jurrus, E.; Paiva, A. R.; Watanabe, S.; Anderson, J. R.; Jones, B.W.; Whitaker, R. T.; Jorgensen, E. M.; Marc, R. E.; and Tasdizen, T. 2010. Detection of neuron membranes in electron microscopy images using a serial neural network architecture. Medical image analysis 14(6):770-783.
13. Kaynig, V.; Fuchs, T. J.; and Buhmann, J. M. 2010. Geometrical consistent 3d tracing of neuronal processes in sstem data. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2010. Springer. 209-216.
14. Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097-1105.
15. Kumar, R.; Vazquez-Reina, A.; and Pfister, H. 2010. Radonlike features and their application to connectomics. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, 186-193. IEEE.
16. Laptev, D.; Vezhnevets, A.; Dwivedi, S.; and Buhmann, J. M. 2012. Anisotropic sstem image segmentation using dense correspondence across sections. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012. Springer. 323-330.
17. Lee, C.-Y.; Xie, S.; Gallagher, P.; Zhang, Z.; and Tu, Z. 2014. Deeply-supervised nets. arXiv preprint arXiv:1409.5185.
18. Liu, T.; Jones, C.; Seyedhosseini, M.; and Tasdizen, T. 2014. A modular hierarchical approach to 3d electron microscopy image segmentation. Journal of neuroscience methods 226:88-102.
19. Long, J.; Shelhamer, E.; and Darrell, T. 2014. Fully convolutional networks for semantic segmentation. arXiv preprint arXiv:1411.4038.
20. Mishchenko, Y. 2009. Automation of 3d reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs. Journal of neuroscience methods 176(2):276-289.
21. Nunez-Iglesias, J.; Kennedy, R.; Parag, T.; Shi, J.; Chklovskii, D. B.; and Zuo, X.-N. 2013. Machine learning of hierarchical clustering to segment 2d and 3d images. PloS one 8(8):08.
22. Rand, W. M. 1971. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association 66(336):846-850.
23. Ronneberger, O.; Fischer, P.; and Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597.
24. Seung, H. S. 2011. Neuroscience: Towards functional connectomics. Nature 471(7337):170-172.
25. Simonyan, K., and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
26. Sporns, O.; Tononi, G.; and Kötter, R. 2005. The human connectome: A structural description of the human brain. PLoS Comput Biol 1(4):e42.
27. Stollenga, M. F.; Byeon, W.; Liwicki, M.; and Schmidhuber, J. 2015. Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation. arXiv preprint arXiv:1506.07452.
28. Uzunbaş, M. G.; Chen, C.; and Metaxsas, D. 2014. Optree: A learning-based adaptive watershed algorithm for neuron segmentation. In Medical Image Computing and Computer- Assisted Intervention-MICCAI 2014. Springer. 97-105.
29. Wang, L.; Lee, C.-Y.; Tu, Z.; and Lazebnik, S. 2015. Training deeper convolutional networks with deep supervision. arXiv preprint arXiv:1505.02496.
30. White, J.; Southgate, E.; Thomson, J.; and Brenner, S. 1986. The structure of the nervous system of the nematode caenorhabditis elegans: The mind of a worm. Phil. Trans. R. Soc. Lond 314:1-340.
31. Wu, X. 2015. An iterative convolutional neural network algorithm improves electron microscopy image segmentation. arXiv preprint arXiv:1506.05849.