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Related Experiment Video

Updated: May 2, 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

Anomaly detection scheme for lung CT images using vector quantized variational auto-encoder with support vector data

Zhihui Gao¹, Ryohei Nakayama², Akiyoshi Hizukuri¹

¹Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan.

Radiological Physics and Technology

|October 26, 2024

Summary

This summary is machine-generated.

This study introduces a Vector Quantized-Variational Auto-Encoder with Support Vector Data Description (VQ-VAE with SVDD) for detecting lung lesions in CT images. This novel anomaly detection scheme improves accuracy over conventional methods, aiding in faster screening.

Keywords:

Anomaly detection Lung CT images SVDD VQ-VAE

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Lung CT image analysis is crucial for disease detection.
Accurate anomaly detection in medical images remains a challenge.
Automated systems can assist radiologists in screening.

Purpose of the Study:

To develop an advanced anomaly-detection scheme for lung lesions in CT images.
To enhance the accuracy of lesion identification using deep learning.
To reduce interpretation time in CT screening processes.

Main Methods:

Implementation of a Vector Quantized-Variational Auto-Encoder (VQ-VAE) integrated with Support Vector Data Description (SVDD).
VQ-VAE with SVDD utilizes two encoders and two decoders for latent variable mapping and image reconstruction.
Anomaly score calculation based on image reconstruction error and latent variable distance from a hypersphere center.

Main Results:

The VQ-VAE with SVDD achieved an area under the ROC curve of 0.76.
This represents a significant improvement compared to the conventional VAE, which achieved 0.63 (p < .001).
The proposed method demonstrates higher anomaly detection accuracy.

Conclusions:

VQ-VAE with SVDD offers superior performance for anomaly detection in lung CT images.
The developed method can effectively identify examinees with lesions.
This approach is expected to reduce interpretation time for CT screening.