Fig. 5.

Automatic image quality rating with an FCN and automatic preference articulation with an SNN. a Proposed FCN architecture for quality rating. Each convolutional layer is followed by spatial batch normalization and an exponential linear unit (ELU) activation. All layers but the last one are also followed by maxpooling with kernel 2 × 2. The output is a linear combination of the three last layers, allowing to handle different sizes of features in the image (see Methods). b Example of score maps produced by the FCN for images of the actin cytoskeleton stained with phalloidin-STAR635. Scale bar 1 μm. c Distribution of qualities in the validation sets (top histograms) and error distributions of an FCN trained on the Actin dataset, on test images of the actin cytoskeleton stained with phalloidin-STAR635. The green dots are the expert scores, sorted by value. The blue areas represent the extent of the errors made by the network at every level of quality. d Architecture of the SNN for preference articulation, given two objectives as input. Each fully connected layer contains ten neurons. e Preference articulation function between photobleaching and image quality learned by the two-objective SNN model. f Example of choices made by the two-objective SNN vs. an expert based on the median error given by Euclidean distance (see Methods). g Preference articulation function between photobleaching, image quality and total imaging time learned by the three-objective SNN model. h Example of choices made by the three-objective SNN vs. an expert based on the median error given by Euclidean distance (see Methods). e–h Quality scores from 0 to 1 are expressed in percentage