# Lung Segmentation in Chest X-rays with Res-CR-Net

Source: [Lung Segmentation in Chest X-rays with Res-CR-Net](<https://www.sota2.com/research/paper/68fa68c31e209131cea1e2b6>)

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

Data updated: 2025-10-25T22:45:52.748Z

Authors: Haikal Abdulah, Benjamin Huber, Sinan Lal, Hassan Abdallah, Hamid Soltanian-Zadeh, Domenico L. Gatti

Published: 2020-11-14T04:06:28.000Z

## Abstract

Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for the semantic segmentation of microscopy images, and which does not adopt a U-Net architecture, is very effective at segmenting the lung fields in chest X-rays from either healthy patients or patients with a variety of lung pathologies.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.08655>)

[PDF](<https://arxiv.org/pdf/2011.08655.pdf>)