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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.
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Updated: May 1, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Segmentation-enhanced multi-scale deep hashing for chest X-ray image retrieval.

Linmin Wang¹, Qianqian Wang¹, Xiaochuan Wang¹

¹Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC, USA.

Medical Image Analysis

|December 30, 2025

Summary

This study introduces a new deep hashing framework for retrieving chest X-ray (CXR) images. The method enhances multi-scale feature extraction using lung segmentation and classification for improved COVID-19 diagnosis.

Keywords:

Chest X-ray image Deep hashing Retrieval Segmentation

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Chest X-ray (CXR) imaging is vital for COVID-19 diagnosis, aiding in rapid lung damage identification.
Deep hashing improves retrieval from large CXR databases but often uses single-scale features, missing multi-scale contextual information.
Integrating lung segmentation and image classification into CXR feature extraction is an underexplored area with potential benefits.

Purpose of the Study:

To develop a novel segmentation-enhanced multi-scale deep hashing (SMDH) framework for automated CXR image retrieval.
To improve feature extraction by incorporating multi-scale contextual information and auxiliary tasks like lung segmentation and classification.
To enhance the efficiency and accuracy of retrieving relevant CXR images for clinical analysis.

Main Methods:

Developed a SMDH framework with distinct feature extraction and image retrieval modules.
Employed a multi-scale neural network architecture for deep mining and fusion of semantic information from CXR images.
Utilized lung segmentation and image classification as auxiliary tasks to guide feature extraction and capture rich anatomical/semantic insights.

Main Results:

The SMDH framework effectively mines and integrates multi-scale semantic information from CXR images.
Auxiliary tasks of lung segmentation and classification enhanced the feature extraction process.
Experiments demonstrated that SMDH outperforms existing state-of-the-art methods on the COVID-QU-Ex dataset.

Conclusions:

The proposed SMDH framework offers an effective approach for automated CXR image retrieval.
Integrating multi-scale features and auxiliary tasks significantly improves the representation of CXR images.
This method provides a more comprehensive tool for healthcare professionals in pandemic analysis and diagnosis.