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Related Experiment Video

Updated: Jun 23, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Coronary artery stenosis segmentation using U-Net architecture with customised loss function.

Nimra Iman1, Romana Aziz2, Mahwish Ilyas3

  • 1Department of Software Engineering, Faculty of Computing and IT, University of Sargodha, Sargodha, Pakistan.

Scientific Reports
|June 21, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a deep learning framework for automated coronary artery stenosis detection using X-ray angiography. The advanced U-Net model significantly improves stenosis segmentation accuracy compared to previous methods.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cardiovascular disorders are the leading global cause of death.
  • Coronary artery stenosis is a significant cardiovascular disorder.
  • Manual stenosis detection is time-consuming and error-prone.

Purpose of the Study:

  • To develop a fully automated deep learning framework for binary stenosis segmentation in coronary arteries.
  • To evaluate the performance of different U-Net architectures for this task.
  • To improve the accuracy and efficiency of stenosis detection.

Main Methods:

  • Utilized a deep learning framework based on U-Net architectures.
  • Compared standard U-Net, U-Net with squeeze-and-excitation blocks, and U-Net with dense blocks.
Keywords:
Artery stenosisCoronaryDeep learningSegmentationUNET

Related Experiment Videos

Last Updated: Jun 23, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

  • Employed a custom loss function and data augmentation on the ARCADE dataset.
  • The dataset includes X-ray angiography images from 1,500 patients.

    • Main Results:

    • The U-Net incorporating dense blocks achieved the best performance.
    • The model achieved a precision of 0.5985, recall of 0.6319, and F1 Score of 61.47%.
    • This represents a significant improvement over the previous F1 Score of 53.4%.

    Conclusions:

    • The proposed deep learning framework shows promising performance for automated stenosis segmentation.
    • The method successfully segments stenotic regions despite challenges like small vessel size and low contrast.
    • This automated approach can address the limitations of manual stenosis detection.