Learning Guided Electron Microscopy with Active Acquisition
Name
2101.02746.pdf
Description
Accepted version
Size
1.94 MB
Format
Adobe PDF
Checksum (MD5)
7673deb76513ec44ec5fcc7b893ba041
Author(s)
Mi, Lu
Wang, Hao
Meirovitch, Yaron
Schalek, Richard
Turaga, Srinivas C.
Lichtman, Jeff W.
Samuel, Aravinthan D. T.
Shavit, Nir N.
Date Issued
September 2020
Journal
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Citation
Mi, Lu et al. "Learning Guided Electron Microscopy with Active Acquisition." MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, 12265, Springer, 2020, 77-87. © 2020 Springer Nature
Version
Author's final manuscript
Abstract
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. In this paper, we show how to use deep learning to accelerate and optimize single-beam SEM acquisition of images. Our algorithm rapidly collects an information-lossy image (e.g. low resolution) and then applies a novel learning method to identify a small subset of pixels to be collected at higher resolution based on a trade-off between the saliency and spatial diversity. We demonstrate the efficacy of this novel technique for active acquisition by speeding up the task of collecting connectomic datasets for neurobiology by up to an order of magnitude. Code is available at https://github.com/lumi9587/learning-guided-SEM.
Description
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12265).
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-59722-1_8
