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Incremental Machine Learning Based Segmentation with Expert-in-the-Loop Workflow Using FIB-SEM Imagery

Published online by Cambridge University Press:  30 July 2020

Rengarajan Pelapur
Rengarajan Pelapur*
Affiliation:
Thermo Fisher Scientific, Houston, Texas, 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. 3100 - 3103
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Copyright
Copyright © Microscopy Society of America 2020

References

Arganda, I., et al. .. “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.” Bioinformatics, 2017: 2424-2426.10.1093/bioinformatics/btx180CrossRefGoogle Scholar
Waller, L., and T. Lei “Computational imaging: Machine learning for 3D microscopy.” Nature, 2015: 416-417.10.1038/523416aCrossRefGoogle Scholar
Pelapur, R., and Heck, M. M.. “3D Platform for Machine Learning Based Segmentation and Visualization Using FIB-SEM Imagery.” M&M, 2019: 214-215.10.1017/S1431927619001806CrossRefGoogle Scholar
Mitochondria Detection in EM Stacks. cvlab.epfl.ch/data/data-em/Google Scholar
Lucchi; Y, A.. Li; P. Fua : Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets. 2013. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA, June 23-28, 2013. DOI : 10.1109/Cvpr.2013.259.Google Scholar
Kreshuk, A., Straehle, C., et al. . “Automated segmentation of synapses in 3D EM data.” In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 220-223. IEEE, 2011.10.1109/ISBI.2011.5872392CrossRefGoogle Scholar
Sommer, C., et al. .. “Interactive learning and segmentation tool kit.” Systems Biology of Human Disease (2010): 230-33.Google Scholar
Nguyen, H. and Ji, Q.. Shape-Driven Three-Dimensional Watersnake Segmentation of Biological Membranes in Electron Tomography. IEEE TMI, 616–628, 2008.Google ScholarPubMed
Paiva, E., et al. ., 2010. Detection of neuron membranes in electron microscopy images using a serial neural network architecture. Medical image analysis, 14(6), pp.770-783.Google Scholar
Ciresan et, D.. al, 2012. Deep neural networks segment neuronal membranes in electron microscopy images. ANIPS, pp. 2843-2851.Google Scholar
Avizo, XP and. (Feb 24, 2019)Google Scholar
Lindeberg, T.. Scale-space theory in computer vision. Springer , 2013Google Scholar
RFLib, http://vision.mas.ecp.fr/Personnel/teboul/RFlib.phpGoogle Scholar
Dagum, L. et al. . OpenMP: an industry standard API for shared-memory programming. IEEE CSE pp.46-55, 1998 10.1109/99.660313CrossRefGoogle Scholar
The Xtra Library, https://xtras.amira-avizo.com/.Google Scholar
Taha, A.A. et al. . Metrics for evaluating 3D medical image segmentation: BMC MI, p.29, 201510.1186/s12880-015-0068-xCrossRefGoogle Scholar
Open Inventor, http://developer.openinventor.com/Google Scholar