Fig. 6
From: A machine learning approach for online automated optimization of super-resolution optical microscopy
Fully automated optimization system for STED imaging. a Scheme of the fully automated system where both networks interact with Kernel TS to automatically select the next imaging parameters (SNN) and rate the quality of the obtained images (FCN). b Cumulative regret (related to the quantity of low-quality images, i.e. below 60%, or high photobleaching, i.e. over 60%) obtained during FA optimization on fixed neurons stained with phalloidin-STAR635 (actin), bassoon-STAR635P and tubulin-STAR635P. Note: The regret is computed using quality scores given independently by an expert on the resulting images. c Parameter configurations selected by Kernel TS during optimization using Bassoon, Tubulin, and Actin proteins. d Evolution of the objective values during FA optimization sequences of live-cell imaging. Each box corresponds to the binned objective values of ten images. For LifeAct-GFP FA optimization improved the quality while controlling the photobleaching and pixel dwelltime. For PSD95-FingR-GFP it allowed to maintain the quality level while reducing the photobleaching and pixel dwelltime. In the case of GFP-αCaMKII it improved the quality while controlling the photobleaching, at the price of increasing the pixel dwelltime. Quality scores from 0 to 1 are expressed in percentage. e Images acquired without human intervention during FA optimization sequences on fixed and live neurons. The dotted line delimits the confocal (lower left) from the STED (upper right) images. Scale bar 1 μm
