Automated Result / Cost Optimization

What is ARCO about?

It's about Life Science Automation 2.0. Improvements in the capacity to perform and analyze time-resolved experiments is a fundamental challenge in quantitative biology, with automation providing the eventual solution. We introduce an algorithm, termed Automated Result / Cost Optimization (ARCO), as an universally-applicable approach based on an operator-defined experiment result. We applied ARCO with LifeXplorer, an adaptable image processing and microscopy hardware control framework, and benchmarked against two intracellular events influenced by imaging induced phototoxic stress: mitochondrial energetics and the reassembly of the Golgi. ARCO optimization significantly increased the information content and accuracy of live-cell experimental results, while reducing phototoxicity 3-6 fold. With ARCO we introduce an overall system optimization for microscopy data acquisition and analysis.

Schematic for the general three-step workflow of the ARCO algorithm

ARCO - concept

It maximizes a specified result and minimizes the costs by evaluating the outcome with different data acquisition and analysis parameter settings (here illustrated with different light exposures). First, the result therefore has to be defined, using relevant existing knowledge about the measured quantities (such as geometry, brightness, etc.). Second, multiple analysis methods are run with multiple parameter sets for multiple acquisition parameters. Third, the parameter configuration for acquisition and analysis is configured, which maximizes the previously defined result (i.e. cell count) and minimizes the costs (i.e. light exposure). Finally, ARCO provides an optimized output for a specific biological question

System optimization basis for Automated Result / Cost Optimization (ARCO) algorithm

ARCO is based on the following optimization equations:


Parameter reduction assuming the parameters can be separated:



Two separate application domains of ARCO: data analysis and data acquisition

(a) The microscope’s parameters are changed automatically and the parameter configuration with the lowest costs and the highest result is set automatically. (b) The first, fundamental step for ARCO optimization requires result definition. Shown are example workflows for detection of the nuclei and Golgi complexes. Definitions for organelles are determined by size and mean brightness features. (c) Different algorithms for segmentation are run with different parameter configurations. Based on the result definition, the segmentation results are filtered and only valid objects remain. The algorithm and parameter configuration with the highest amount of valid objects is selected. (d) Based on a reference imaging position, the segmentation algorithm is applied to a range of exposure times, and the lowest exposure time with best result (i.e.  95% of the area is segmented) is used for data collection, for each time point.