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AI-based Brain Image Segmentation Using Synthesized Data

Published online by Cambridge University Press:  30 July 2020

Pouya Tavousi ,
zahra Shahbazi  and
Sina Shahbazmohamadi
Pouya Tavousi
Affiliation:
University of Connecticut, Storrs, Connecticut, United States
zahra Shahbazi
Affiliation:
Manhattan College, Storrs, Connecticut, United States
Sina Shahbazmohamadi
Affiliation:
REFINE lab/ University of Connecticut, Storrs, Connecticut, United States

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 694 - 697
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Copyright
Copyright © Microscopy Society of America 2020

References

Kasthuri, N., Hayworth, K. J., Berger, D. R., Schalek, R. L., Conchello, J. A., Knowles-Barley, S., Lee, D., Vázquez-Reina, A., Kaynig, V., Jones, T. R. and others, “Saturated reconstruction of a volume of neocortex,” Cell, vol. 162, pp. 648-661, 2015.10.1016/j.cell.2015.06.054CrossRefGoogle Scholar
Zaitoun, N. M. and Aqel, M. J., “Survey on image segmentation techniques,” Procedia Computer Science, vol. 65, pp. 797-806, 2015.10.1016/j.procs.2015.09.027CrossRefGoogle Scholar
Arganda-Carreras, I., Turaga, S. C., Berger, D. R., Cireşan, D., Giusti, A., Gambardella, L. M., Schmidhuber, J., Laptev, D., Dwivedi, S., Buhmann, J. M. and others, “Crowdsourcing the creation of image segmentation algorithms for connectomics,” Frontiers in neuroanatomy, vol. 9, p. 142, 2015.10.3389/fnana.2015.00142CrossRefGoogle Scholar
Vazquez-Reina, A., Gelbart, M., Huang, D., Lichtman, J., Miller, E. and Pfister, H., “Segmentation fusion for connectomics,” in 2011 International Conference on Computer Vision, 2011.10.1109/ICCV.2011.6126240CrossRefGoogle Scholar
Rao, Q., Han, H., Li, W., Shen, L., Chen, X. and Xie, Q., “Automatically segmenting and reconstructing neurons in SEM images,” in 2016 IEEE International Conference on Mechatronics and Automation, 2016.10.1109/ICMA.2016.7558857CrossRefGoogle Scholar
Cabezón, I., Augé, E., Bosch, M., Beckett, A. J., Prior, I. A., Pelegr\́i, C. and Vilaplana, J., “Serial block-face scanning electron microscopy applied to study the trafficking of 8D3-coated gold nanoparticles at the blood--brain barrier,” Histochemistry and cell biology, vol. 148, pp. 3-12, 2017.10.1007/s00418-017-1553-9CrossRefGoogle Scholar
Westenberger, P., “AVIZO-3D visualization framework,” in Geoinformatics Conference, 2008.Google Scholar
Makovetsky, R., Piche, N. and Marsh, M., “Dragonfly as a Platform for Easy Image-based Deep Learning Applications,” Microscopy and Microanalysis, vol. 24, pp. 532-533, 2018.10.1017/S143192761800315XCrossRefGoogle Scholar
Srikanthan, A. P. and others, “REVERSE ENGINEERING OF MACHINE PART BY PARAMETRIC MANNER,” 2019.Google Scholar
Favata, J. and Shahbazmohamadi, S., “Realistic non-destructive testing of integrated circuit bond wiring using 3-D X-ray tomography, reverse engineering, and finite element analysis,” Microelectronics Reliability, vol. 83, pp. 91-100, 2018.10.1016/j.microrel.2018.02.015CrossRefGoogle Scholar