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Related Experiment Video

Updated: Jun 29, 2026

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

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Published on: November 30, 2022

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic

Zonghan Lyu^1,2, Kristin King^1,2, Mostafa Rezaeitaleshmahalleh^1,2

¹Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America.

Biomedical Physics & Engineering Express

|August 25, 2023

Summary

Deep learning models like CACU-Net can automate abdominal aortic aneurysm (AAA) segmentation from CT scans, significantly reducing model creation time and enabling faster computational hemodynamics analysis.

Keywords:

abdominal aortic aneurysm computational fluid dynamics computational hemodynamics deep-learning image 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
Computational Fluid Dynamics
Artificial Intelligence in Medicine

Background:

Computational hemodynamics analysis of abdominal aortic aneurysms (AAA) requires patient-specific models.
Manual segmentation of computed tomography angiography (CTA) scans for AAA model creation is time-consuming, hindering clinical application.
Automated segmentation methods are needed to accelerate the process.

Purpose of the Study:

To investigate the feasibility of deep-learning-based image segmentation for automating AAA model creation.
To compare the performance of ARU-Net and CACU-Net for AAA segmentation in computational hemodynamics.
To assess the time reduction and accuracy of automated segmentation compared to manual methods.

Main Methods:

Two deep learning models, ARU-Net and CACU-Net, were employed for image segmentation of 30 CTA scans.
Morphological features and hemodynamic metrics were compared between deep learning and manual segmentation models.
Performance was evaluated using DICE scores, correlation coefficients, and Bland-Altman analysis.

Main Results:

Both ARU-Net and CACU-Net achieved a DICE score of 0.916 and correlation above 0.95, indicating high agreement with manual segmentation.
Bland-Altman analysis confirmed good agreement between automated and manual segmentation results.
Automated model generation time was reduced from approximately 2 hours to 10 minutes, with CACU-Net showing superior accuracy and speed.

Conclusions:

Automated image segmentation using deep learning significantly reduces the time and cost associated with patient-specific AAA model creation.
CACU-Net and ARU-Net are effective deep learning methods for AAA segmentation.
CACU-Net demonstrates superior performance in accuracy and efficiency for computational hemodynamics applications.