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Related Experiment Video

Updated: Jun 12, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs.

Kiri S Newson¹, David M Benoit², Andrew W Beavis^3,4,5

¹Department of Physics and Mathematics, University of Hull, Hull, United Kingdom.

Peerj. Computer Science

|August 15, 2024

Summary

This summary is machine-generated.

A new Encoder-Decoder convolutional neural network (ED-CNN) model offers automated segmentation of COVID-19 computerised tomography (CT) scans. This accessible tool accurately delineates lung infections, providing a faster alternative to manual contouring.

Keywords:

Autoencoder Automated segmentation CNN COVID-19 Encoder-decoder Lung CT Lung segmentation Machine learning Simple segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonology

Background:

Manual segmentation of COVID-19 infected regions in CT scans is time-consuming and resource-intensive.
Existing deep learning models for segmentation often involve complex architectures and extensive training requirements.
There is a need for accessible and efficient automated segmentation tools for clinical application.

Purpose of the Study:

To develop and evaluate a simple, reproducible Encoder-Decoder convolutional neural network (ED-CNN) model for automated segmentation of COVID-19 CT data.
To compare the performance of the proposed ED-CNN model against existing, more complex deep learning approaches.
To demonstrate the utility of automated segmentation in clinical workflows for faster assessment of lung infection.

Main Methods:

Application of a compact Encoder-Decoder convolutional neural network (ED-CNN) model.
Training and validation on COVID-19 computerised tomography (CT) datasets.
Evaluation of segmentation performance using metrics such as Specificity, Accuracy, and Mean Absolute Error.

Main Results:

The ED-CNN model achieved high segmentation accuracy, with Specificity (Sp) = 0.996 ± 0.001, Accuracy (Acc) = 0.994 ± 0.002, and Mean Absolute Error (MAE) = 0.0075 ± 0.0005.
The proposed model utilizes significantly fewer parameters (approx. 49k) compared to other complex deep learning networks.
Automated segmentation successfully delineated infected regions in thoracic CT scans, comparable to results from more complex models.

Conclusions:

The developed ED-CNN model provides a high-quality, automated solution for segmenting COVID-19 CT scans.
Its simplicity, efficiency, and low parameter count make it accessible for real-world applications and personalized medicine workflows.
Automated segmentation can significantly expedite the diagnosis and treatment planning process, saving valuable time and resources.