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Related Experiment Video

Updated: Jan 21, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Deep learning-based digital subtraction angiography image generation.

Yufeng Gao^1,2, Yu Song^1,2, Xiangrui Yin^1,2

¹Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China.

International Journal of Computer Assisted Radiology and Surgery

|August 2, 2019

Summary

This summary is machine-generated.

This study introduces a deep learning method to create digital subtraction angiography (DSA) images from a single live image, reducing artifacts from patient movement and improving diagnostic accuracy.

Keywords:

Adversarial network Artifact elimination Digital subtraction angiography Residual dense block

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Diagnostics

Background:

Digital subtraction angiography (DSA) is crucial for diagnosing cardiovascular diseases.
Patient movement during DSA imaging can cause artifacts, hindering diagnosis.
Current methods often require a mask image, adding complexity.

Purpose of the Study:

To develop a deep learning method for generating DSA images from a single live image.
To eliminate the need for a mask image in DSA acquisition.
To reduce image artifacts caused by patient motion.

Main Methods:

Utilized a dataset of over 600 sequences from 100+ subjects for head and leg experiments.
Employed residual dense blocks (RDBs) to extract high-level features for direct DSA image generation.
Incorporated a supervised generative adversarial network (GAN) strategy for enhanced vessel detail.

Main Results:

Deep learning successfully generated DSA images from single live images in head and leg experiments.
The proposed method produced fewer artifacts compared to other models, improving diagnostic suitability.
Quantitative metrics (PSNR, SSIM, FSIM) demonstrated strong performance on both datasets.

Conclusions:

The developed model effectively extracts vessels from single live images, circumventing artifacts from traditional mask subtraction.
This deep learning approach offers superior performance over existing methods for artifact-free DSA image generation.