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Related Experiment Video

Updated: Sep 11, 2025

Clock Scan Protocol for Image Analysis: ImageJ Plugins

Clock Scan Protocol for Image Analysis: ImageJ Plugins

Published on: June 19, 2017

Analysis of smart imaging runtime.

Thomas Athey¹, Shashata Sawmya², Yaron Meirovitch³

¹Massachusetts Institute of Technology, Cambridge, MA, USA. tathey_1@mit.edu.

Applied Microscopy

|August 14, 2025

Summary

This summary is machine-generated.

Smart microscopy significantly speeds up electron microscopy connectomics by intelligently selecting regions for imaging. Analyzing workflow parameters reveals how to maximize time savings with this advanced imaging technique.

Keywords:

Active acquisition Deep learning Electron microscopy Mixed-precision Parallelization Runtime

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Last Updated: Sep 11, 2025

Clock Scan Protocol for Image Analysis: ImageJ Plugins

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Published on: June 19, 2017

Time-lapse Imaging of Bacterial Swarms and the Collective Stress Response

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Published on: May 23, 2020

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

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Published on: June 16, 2014

Area of Science:

Microscopy
Neuroscience
Computational Biology

Background:

Smart microscopy accelerates imaging by rapid acquisition, prediction of key subregions, and selective re-imaging.
This approach has demonstrated reduced imaging beam time in electron microscopy connectomics.
However, the actual speedup is contingent upon various imaging workflow parameters.

Purpose of the Study:

To conduct the first runtime analysis comparing traditional and smart microscopy.
To investigate how imaging workflow parameters influence time savings in smart microscopy.
To provide a tool for calculating theoretical time savings based on user-defined parameters.

Main Methods:

Developed a graphical user interface (GUI) application for calculating theoretical time savings.
Input parameters for the GUI include details of the imaging workflow.
Measured end-to-end runtime of SmartEM acquisition on an electron microscope.

Main Results:

Identified key imaging workflow parameters that can significantly magnify or diminish time savings.
Demonstrated two strategies for faster acquisition: mixed-precision neural networks and parallelization of microscope and support computer operations.
The GUI application provides a quantitative estimate of potential time savings.

Conclusions:

Smart microscopy offers substantial time savings in electron microscopy connectomics.
Understanding and optimizing imaging workflow parameters is crucial for maximizing efficiency.
Implementation of advanced computational strategies like mixed-precision networks and parallelization further enhances acquisition speed.