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Updated: Aug 4, 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

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation.

Yu Qiu, Yun Liu, Shijie Li

IEEE Transactions on Neural Networks and Learning Systems

|April 4, 2023

Summary

This summary is machine-generated.

MiniSeg, a new lightweight model, efficiently segments COVID-19 from CT scans using an attentive hierarchical spatial pyramid module. This deep learning approach offers high accuracy with fewer parameters, reducing overfitting and enabling rapid deployment for pandemic screening.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Science

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Deep learning for COVID-19 segmentation from CT images shows promise but faces challenges with data requirements and computational intensity.
Existing methods often struggle with overfitting due to limited COVID-19 training data.

Purpose of the Study:

To develop a lightweight and efficient deep learning model for segmenting COVID-19 infected areas in CT images.
To address the limitations of traditional deep learning models in terms of data hunger, overfitting, and computational cost.
To introduce a novel architecture that balances feature extraction and efficiency for practical deployment.

Main Methods:

Proposed MiniSeg, a lightweight model featuring an attentive hierarchical spatial pyramid (AHSP) module for multiscale learning.
Implemented a two-path (TP) encoder combining AHSP for contextual features and a shallow convolutional path for fine details.
Utilized a simple decoder for the final COVID-19 segmentation task.

Main Results:

MiniSeg achieved high accuracy in COVID-19 segmentation from CT images.
The model demonstrated reduced overfitting due to its small parameter count (83k).
MiniSeg exhibited high efficiency, facilitating easy deployment and development.

Conclusions:

MiniSeg offers an efficient and accurate solution for COVID-19 segmentation in CT images.
The lightweight design makes it suitable for quick deployment in clinical settings.
The proposed AHSP module and TP encoder effectively capture essential features for segmentation.