Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images
This work addresses segmentation for microscopy imaging, offering an incremental improvement over existing U-Net-based methods.
The paper tackled semantic segmentation of microscopy images by introducing Res-CR-Net, a residual network with novel blocks using separable atrous convolutions or convolutional LSTMs, achieving optimized training with minimal hyperparameter tuning.
Deep Neural Networks (DNN) have been widely used to carry out segmentation tasks in both electron and light microscopy. Most DNNs developed for this purpose are based on some variation of the encoder-decoder type U-Net architecture, in combination with residual blocks to increase ease of training and resilience to gradient degradation. Here we introduce Res-CR-Net, a type of DNN that features residual blocks with either a bundle of separable atrous convolutions with different dilation rates or a convolutional LSTM. The number of filters used in each residual block and the number of blocks are the only hyperparameters that need to be modified in order to optimize the network training for a variety of different microscopy images.