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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: Jul 10, 2025

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

CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans.

Tanvir Mahmud¹, Md Awsafur Rahman¹, Shaikh Anowarul Fattah¹

¹Department of Electrical and Electronic EngineeringBangladesh University of Engineering and Technology Dhaka 1000 Bangladesh.

IEEE Transactions on Artificial Intelligence

|November 20, 2023

Summary

This study introduces CovSegNet, an efficient neural network for automated COVID-19 lesion segmentation in CT scans. It improves accuracy and speed, aiding faster diagnosis and reducing the impact of the virus.

Keywords:

Artificial intelligence (AI)biomedical imaging computer aided analysis image segmentation neural networks

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate COVID-19 diagnosis and severity assessment rely on lung lesion segmentation from CT scans.
Traditional segmentation methods struggle with complex lesion features and computational demands of 3D data.
Limitations include information loss, vanishing gradients, and increased semantic gaps in U-Net architectures.

Purpose of the Study:

To propose an automated COVID-19 lesion segmentation scheme using an efficient neural network, CovSegNet.
To overcome limitations of traditional segmentation methods in terms of accuracy and computational complexity.
To enhance diagnostic speed and accuracy for COVID-19 through improved lesion extraction.

Main Methods:

Developed CovSegNet, a novel neural network architecture with horizontal and vertical expansion.
Implemented a two-phase training strategy using 2D and 3D networks for enhanced feature extraction.
Integrated multiscale feature maps and a multiscale fusion module with pyramid fusion for contextual information preservation.

Main Results:

CovSegNet achieved outstanding performance on three public datasets, outperforming state-of-the-art methods.
Demonstrated significant performance improvement (8.4% in averaged dice measurement) over existing approaches.
The proposed scheme effectively segments challenging COVID-19 lesions with diffused and blurred edges.

Conclusions:

CovSegNet offers an efficient and accurate solution for automated COVID-19 lesion segmentation from CT scans.
The two-phase training and novel architecture address limitations of traditional methods, improving diagnostic capabilities.
This approach can be extended to various medical imaging segmentation tasks, aiding faster diagnosis and disease management.