# Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients

arXiv: https://arxiv.org/abs/2104.02042 · HTML: https://mlsanity.com/paper/2104.02042/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation](https://mlsanity.com/paper/2004.12537/) (2020)
- [Lung Infection Quantification of COVID-19 in CT Images with Deep Learning](https://mlsanity.com/paper/2003.04655/) (2020)
- [COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution](https://mlsanity.com/paper/2004.10987/) (2020)
- [Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net](https://mlsanity.com/paper/2007.04774/) (2020)
- [An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism](https://mlsanity.com/paper/2004.06673/) (2020)
- [Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients](https://mlsanity.com/paper/2007.15546/) (2020)
- [CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network](https://mlsanity.com/paper/2010.08582/) (2020)
- [Quantitative analysis of image quality in low-dose CT imaging for Covid-19 patients](https://mlsanity.com/paper/2102.08128/) (2021)

## Built on by (most cited first, 3 of 3)

- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [A novel unsupervised covid lung lesion segmentation based on the lung tissue identification](https://mlsanity.com/paper/2202.12148/) (2022)
- [A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging](https://mlsanity.com/paper/2203.03336/) (2022)

## Abstract (arXiv)

Automated semantic image segmentation is an essential step in quantitative image analysis and disease diagnosis. This study investigates the performance of a deep learning-based model for lung segmentation from CT images for normal and COVID-19 patients. Chest CT images and corresponding lung masks of 1200 confirmed COVID-19 cases were used for training a residual neural network. The reference lung masks were generated through semi-automated/manual segmentation of the CT images. The performance of the model was evaluated on two distinct external test datasets including 120 normal and COVID-19 subjects, and the results of these groups were compared to each other. Different evaluation metrics such as dice coefficient (DSC), mean absolute error (MAE), relative mean HU difference, and relative volume difference were calculated to assess the accuracy of the predicted lung masks. The proposed deep learning method achieved DSC of 0.980 and 0.971 for normal and COVID-19 subjects, respectively, demonstrating significant overlap between predicted and reference lung masks. Moreover, MAEs of 0.037 HU and 0.061 HU, relative mean HU difference of -2.679% and -4.403%, and relative volume difference of 2.405% and 5.928% were obtained for normal and COVID-19 subjects, respectively. The comparable performance in lung segmentation of the normal and COVID-19 patients indicates the accuracy of the model for the identification of the lung tissue in the presence of the COVID-19 induced infections (though slightly better performance was observed for normal patients). The promising results achieved by the proposed deep learning-based model demonstrated its reliability in COVID-19 lung segmentation. This prerequisite step would lead to a more efficient and robust pneumonia lesion analysis.