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

Source: [Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images](<https://www.sota2.com/research/paper/68fa5f021e209131ce9e6e0a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:05:52.386Z

Authors: Hassan Abdallah, Asiri Liyanaarachchi, Maranda Saigh, Samantha Silvers, Suzan Arslanturk, Douglas J. Taatjes, Lars Larsson, Bhanu P. Jena, Domenico L. Gatti

Published: 2020-04-14T21:21:01.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.08246>)

[PDF](<https://arxiv.org/pdf/2004.08246.pdf>)