A context-aware delayed agglomeration framework for electron microscopy segmentation

PLoS ONE

Volumn 10, Issue 5, 2015, Pages

  Parag, Toufiq ^a   Chakraborty, Anirban ^b   Plaza, Stephen ^a   Scheffer, Louis ^a  

^a HOWARD HUGHES MEDICAL INSTITUTE   (United States)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRON MICROSCOPY; NERVE CELL; PREDICTION; ALGORITHM;

ALGORITHMS; MICROSCOPY, ELECTRON;

EID: 84946599449     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0125825     Document Type: Article

References (26)

1. Helmstaedter M., Briggman K., Turaga S., Jain V., Seung S., Denk W.: Connectomics reconstruction of the inner plexiform layer in the mouse retina. Nature 500 (7461) (2013) 168-174 doi: 10.1038/ nature12346 PMID: 23925239
2. Takemura S. Y., Bharioke A., Lu Z., Nern A., Vitaladevuni S., Rivlin P. K., et al.: A visual motion detection circuit suggested by Drosophila connectomics. Nature 500 (7461) (2013) 175-181 doi: 10.1038/ nature12450 PMID: 23925240
3. Arbelaez P., Maire M., Fowlkes C., Malik J.: Contour detection and hierarchical image segmentation. PAMI, IEEE Transactions on 33(5) (2011) 898-916 doi: 10.1109/TPAMI.2010.161
4. Krahenbuhl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. In: NIPS. (2011)
5. Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Associative hierarchical crfs for object class image segmentation. In: ICCV. (2009)
6. Andres B., Köthe U., Helmstaedter M., Denk W., Hamprecht F.: Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. Pattern Recognition 5096(15) (2008) 142-152 doi: 10.1007/978-3-540-69321-5-15
7. Andres, B., Kroeger, T., Briggman, K., Denk, W., Korogod, N., Knott, G., Koethe, U., Hamprecht, F.: Globally optimal closed-surface segmentation for connectomics. In: ECCV. (2012)
8. Chklovskii D.B., Vitaladevuni S., Scheffer L.K.: Semi-automated reconstruction of neural circuits using electron microscopy. Current Opinion in Neurobiology 20(5) (2010) 667-675 doi: 10.1016/j.conb.2010. 08.002 PMID: 20833533
9. Funke, J., Andres, B., Hamprecht, F., Cardona, A., Cook, M.: Efficient automatic 3d-reconstruction of branching neurons from em data. In: CVPR. (2012)
10. Jain V., Turaga S.C., Briggman K., Helmstaedter M.N., Denk W., Seung H.S.: Learning to agglomerate superpixel hierarchies. In: NIPS 24. (2011) 648-656
11. Vazquez-Reina, A., Gelbart, M., Huang, D., Lichtman, J., Miller, E., Pfister, H.: Segmentation fusion for connectomics. In: ICCV. (2011)
12. Beucher, S., Meyer, F.: The Morphological Approach to Segmentation: The Watershed Transformation. Mathematical Morphology in Image Processing (1993) 433-481
13. Liu, T., Jurrus, E., Seyedhosseini, M., Ellisman, M., Tasdizen, T.: Watershed merge tree classification for electron microscopy image segmentation. In: ICPR. (2012)
14. Uzunbas, M., Chen, C., Metaxsas. D.: Optree: A Learning-Based Adaptive Watershed Algorithm for Neuron Segmentation. In: MICCAI. (2014)
15. 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) (2013). doi: 10.1371/journal.pone.0071715 PMID: 23977123
16. Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: NIPS. (2012)
17. Jain, V., Bollmann, B., Richardson, M., Berger, D., Helmstaedter, M., Briggman, K., et al: Boundary learning by optimization with topological constraints. In: CVPR. (2010)
18. Jurrus E., Paiva A., Watanabe S., Anderson J., Jones B., Whitaker R., et al: Detection of neuron membranes in electron microscopy images using a serial neural network architecture. Medical Image Analysis 14(6) (2010) 770-783 doi: 10.1016/j.media.2010.06.002 PMID: 20598935
19. Liu T., Jones C., Seyedhosseini M., Tasdizen T.: A modular hierarchical approach to 3D electron microscopy image segmentation. Journal of Neuroscience Methods 226(15) (2014) 88-102. doi: 10. 1016/j.jneumeth.2014.01.022 PMID: 24491638
20. Parag, T., Plaza, S., Sche er, L.: Small smaple learning of superpixel classifiers for EM segmentation. In: MICCAI. (2014)
21. Helmstaedter M.: Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nat Methods 10(6) (2013) 501-7 doi: 10.1038/nmeth.2476 PMID: 23722209
22. Lucchi A., Smith K., Achanta R., Knott G., Fua P.: Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features. Medical Imaging, IEEE Transactions on 31(2) (2012) 474-486 doi: 10.1109/TMI.2011.2171705
23. Breiman L.: Random forests. Machine Learning 45(1) (October 2001) 5-32 doi: 10.1023/ A:1010933404324
24. Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A.: "ilastik: Interactive learning and segmentation toolkit". In: ISBI. (2011)
25. Meila, M.: Comparing clusterings by the variation of information. In: COLT'03. (2003) 173-187
26. Plaza, S., Parag, T., Huang, G., Olbris, D., Saunders, M., Rivlin, P.: Annotating Synapses in Large EM Datasets. arXiv:1409.1801. (2014)