Learning Guided Electron Microscopy with Active Acquisition

Single-beam scanning electron microscopes (SEM) are widely used to acquire massive datasets for biomedical study, material analysis, and fabrication inspection. Datasets are typically acquired with uniform acquisition: applying the electron beam with the same power and duration to all image pixels, even if there is great variety in the pixels’ importance for eventual use. Many SEMs are now able to move the beam to any pixel in the field of view without delay, enabling them, in principle, to invest their time budget more effectively with non-uniform imaging.

[1]  William R. Gray Roncal,et al.  Saturated Reconstruction of a Volume of Neocortex , 2015, Cell.

[2]  Zhao Zhang,et al.  Deep Learning-Based Point-Scanning Super-Resolution Imaging , 2019, Nature Methods.

[3]  Loic A. Royer,et al.  Content-aware image restoration: pushing the limits of fluorescence microscopy , 2018, Nature Methods.

[4]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[5]  Tim Dahmen,et al.  Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics , 2020, Microscopy and Microanalysis.

[6]  Lu Gan.  Block Compressed Sensing of Natural Images , 2007, 2007 15th International Conference on Digital Signal Processing.

[7]  Nir Shavit,et al.  The big data challenges of connectomics , 2014, Nature Neuroscience.

[8]  Gáspár Jékely,et al.  Content-aware image restoration for electron microscopy. , 2019, Methods in cell biology.

[9]  Sebastien Ourselin,et al.  Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation , 2018 .

[10]  Haim Pearl,et al.  Detection of electrical defects with SEMVision in semiconductor production mode manufacturing , 2016, 2016 27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC).

[11]  Thomas Brox,et al.  Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow , 2018, ECCV.

[12]  Hayk Saribekyan,et al.  Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  A. L. Eberle,et al.  High-resolution, high-throughput imaging with a multibeam scanning electron microscope , 2015, Journal of microscopy.

[14]  Travis A. Jarrell,et al.  The Connectome of a Decision-Making Neural Network , 2012, Science.

[15]  Tim Dahmen,et al.  Feature Adaptive Sampling for Scanning Electron Microscopy , 2016, Scientific Reports.

[16]  Hyrum S. Anderson,et al.  Sparse imaging for fast electron microscopy , 2013, Electronic Imaging.

[17]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  A. Ozcan,et al.  Deep learning enables cross-modality super-resolution in fluorescence microscopy , 2018, Nature Methods.

[19]  Hao Wang,et al.  Training-Free Uncertainty Estimation for Neural Networks , 2019, ArXiv.

[20]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Yee Lian Chew,et al.  Network control principles predict neuron function in the Caenorhabditis elegans connectome , 2017, Nature.

[22]  D. K. Setua,et al.  Material behaviour: Fracture topography of rubber surfaces: an SEM study , 2003 .

[23]  Larry Lindsey,et al.  High-precision automated reconstruction of neurons with flood-filling networks , 2017, Nature Methods.

[24]  Ben Taskar,et al.  Determinantal Point Processes for Machine Learning , 2012, Found. Trends Mach. Learn..

[25]  T. Blumensath,et al.  Theory and Applications , 2011 .

[26]  Moritz Helmstaedter,et al.  High-accuracy neurite reconstruction for high-throughput neuroanatomy , 2011, Nature Neuroscience.

[27]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[28]  P. Nellist.  Scanning Transmission Electron Microscopy , 2020, Definitions.

[29]  A. Crewe,et al.  Scanning transmission electron microscopy * , 1974, Journal of microscopy.