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Deep Neural Networks (DNN) have been widely used to carry out segmentationtasks in both electron and light microscopy. Most DNNs developed for thispurpose are based on some variation of the encoder-decoder type U-Netarchitecture, in combination with residual blocks to increase ease of trainingand resilience to gradient degradation. Here we introduce Res-CR-Net, a type ofDNN that features residual blocks with either a bundle of separable atrousconvolutions with different dilation rates or a convolutional LSTM. The numberof filters used in each residual block and the number of blocks are the onlyhyperparameters that need to be modified in order to optimize the networktraining for a variety of different microscopy images.

Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images