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Related Experiment Video

Updated: Sep 6, 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

2D Anatomical Structure for COVID-19 Medical Images.

Stelios Zimeras¹, Marianna Diomidous²

¹Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Samos, Greece.

Studies in Health Technology and Informatics

|July 1, 2022

Summary

Accurate 3D organ segmentation from CT images aids surgical planning and medical diagnosis. This study applies advanced techniques to organize big data for COVID-19 patient analysis.

Keywords:

Anatomical structures COVID-19 medical images segmentation techniques

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Radiology

Background:

Accurate anatomical structure identification is crucial for clinical applications such as surgical and radiotherapy treatment planning.
Reconstructing 2D/3D organ shapes is essential for medical diagnosis and treatment strategies.
Managing large volumes of medical image data presents significant storage and organization challenges.

Purpose of the Study:

To apply advanced segmentation techniques for defining 3D anatomical structures from medical images.
To address the challenge of organizing and utilizing big data in medical imaging for clinical purposes.
To implement these processes on computed tomography (CT) images from patients diagnosed with COVID-19.

Main Methods:

Development and application of novel image segmentation algorithms.
Utilizing big data storage and organization frameworks for medical imaging.
Processing and analysis of CT scans from COVID-19 patient cohorts.

Main Results:

Demonstrated efficacy of segmentation techniques in defining 3D organ structures.
Successful organization and management of large-scale medical image datasets.
Validation of the approach on CT images relevant to COVID-19 diagnosis.

Conclusions:

Segmentation techniques are vital for efficient big data management in medical imaging.
The proposed methods facilitate improved understanding of anatomical structures for clinical decision-making.
This work provides a foundation for enhanced diagnostic capabilities using segmented CT data from COVID-19 patients.