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Updated: Nov 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a

Karen López-Linares1,2,3, Maialen Stephens1, Inmaculada García1,2

  • 1Vicomtech Foundation, San Sebastián, Spain.

Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting : First International Workshop, MLMECH 2019, and 8Th Joint International Workshop, CVII-STENT 2019, Held in Conj
|June 11, 2021
PubMed
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This summary is machine-generated.

This study demonstrates that Convolutional Neural Networks (CNNs) can effectively segment abdominal aortic aneurysms (AAAs) using only synthetic images. This approach reduces the need for extensive real-world annotated data, proving comparable to traditional methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Abdominal aortic aneurysms (AAAs) are life-threatening vascular conditions requiring accurate imaging for management.
  • Computed Tomography Angiography (CTA) is crucial for AAA assessment, with segmentation vital for risk evaluation.
  • Current deep learning segmentation methods, like Convolutional Neural Networks (CNNs), demand large annotated datasets, posing a significant challenge.

Purpose of the Study:

  • To develop and validate a methodology for training CNNs to segment AAAs using solely synthetic data.
  • To assess the generalization capability of CNNs trained on synthetic data for segmenting real CTA scans.
  • To reduce the dependency on large annotated medical image datasets for AAA segmentation.

Main Methods:

Keywords:
Abdominal aortic aneurysmConvolutional Neural NetworkPrincipal component analysisSegmentationSynthetic images

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  • Generation of synthetic AAA images using a shape model with realistic deformations derived from principal component analysis of registration data.
  • Training a CNN model exclusively on these generated synthetic images.
  • Evaluating the trained CNN's performance in segmenting AAAs from unseen, original CTA scans.

Main Results:

  • CNNs trained with synthetic data achieved segmentation performance comparable to those trained with real annotated images.
  • The proposed methodology demonstrated effective generalization, accurately segmenting AAAs in new CTA scans.
  • The findings suggest synthetic data can significantly mitigate the need for extensive manual annotation.

Conclusions:

  • Training CNNs with synthetic data is a viable and effective strategy for abdominal aortic aneurysm segmentation.
  • This approach holds promise for reducing the data acquisition burden in medical image analysis.
  • The methodology can potentially be extended to other aneurysm types and medical imaging segmentation tasks.