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Automatic Explainable Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography.

Merjulah Roby1, Abu Noman Md Sakib2, Zijie Zhang2

  • 1Department of Mechanical, Aerospace, and Industrial Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA.

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Summary

This study introduces an automated deep learning framework for segmenting abdominal aortic aneurysms (AAA) in CT angiography images, achieving high accuracy and real-time processing speeds for improved screening and analysis.

Keywords:
Abdominal aortic aneurysmcomputed tomography imagingdeep learningexplainable AIimage segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Imaging

Background:

  • Abdominal aortic aneurysm (AAA) screening and analysis require accurate segmentation of vascular structures.
  • Current segmentation methods can be time-consuming and may lack automation.
  • Deep learning offers potential for automated and efficient medical image analysis.

Purpose of the Study:

  • To develop and validate an automated deep learning framework for segmenting abdominal aortic aneurysms (AAA) in contrast-enhanced computed tomography angiography (CTA) images.
  • To assess the framework's accuracy, efficiency, and potential for clinical application in AAA screening and analysis.

Main Methods:

  • Development of a deep learning framework utilizing a dynamic router and three specialized U-Net models for AAA segmentation.
  • Training and validation on a large dataset (9,080 images) and testing on a separate set (1,560 images).
  • Evaluation of segmentation performance using Dice scores (DS), Intersection over Union (IoU), and Hausdorff distance (HD95).
  • Assessment of processing time for automated segmentation and manual refinement using non-uniform rational B-splines (NURBS).

Main Results:

  • The framework achieved high segmentation accuracy for both aortic lumen and outer wall, with DS of 0.9648/0.9615 and IoU of 0.9324/0.9264.
  • Excellent performance was indicated by HD95 values of 1.3490 mm and 1.3670 mm for lumen and wall, respectively.
  • The automated system processed images rapidly at approximately 17 ± 1 milliseconds per frame.
  • Manual NURBS refinement for complex cases ranged from 3 to 20 seconds per frame.

Conclusions:

  • The automated deep learning framework demonstrates high accuracy and efficiency for segmenting abdominal aortic aneurysms in CTA images.
  • The system's real-time processing capability makes it suitable for clinical screening and analysis.
  • Further research integrating multimodal imaging and optimizing NURBS refinement could enhance accuracy and efficiency.