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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A study of AAA image segmentation technique using geometric active contour model with morphological gradient edge

H C Kim1, Y H Seol, S Y Choi

  • 1Biomedical Engineering, Biomedical Science of Brain Korea 21, Korea University.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary
This summary is machine-generated.

A new method, the proposed geometric active contour model (PGACM), accurately segments abdominal aortic aneurysms (AAA) from CT scans. This technique improves upon existing models for precise AAA size measurement, aiding in treatment decisions.

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Vascular Surgery

Background:

  • Abdominal aortic aneurysm (AAA) poses a significant life-threatening risk.
  • Accurate measurement of AAA dimensions is crucial for effective surgical or endovascular repair.
  • Current segmentation methods may suffer from limitations like image blurring.

Purpose of the Study:

  • To evaluate the feasibility of a novel method for quantitative measurement of abdominal aortic aneurysms (AAA).
  • To introduce and assess the proposed geometric active contour model (PGACM) for AAA segmentation.
  • To compare the performance of PGACM against conventional active contour models.

Main Methods:

  • Developed the proposed geometric active contour model (PGACM), modifying the conventional geometric active contour model (CGACM).
  • PGACM utilizes a morphological gradient edge function to avoid Gaussian filter-induced blurring.
  • Tested the model on three noised synthetic images of varying shapes and compared performance metrics (distance error, mismatched area, execution time) against PACM and CGACM.

Main Results:

  • PGACM demonstrated superior segmentation performance compared to PACM and CGACM.
  • The proposed method achieved better results in terms of normalized minimum distance error, mismatched area, and execution time.
  • Quantitative measurements of AAA regions of interest were successfully driven by the PGACM.

Conclusions:

  • The PGACM method is feasible, accurate, and precise for segmenting abdominal aortic aneurysms (AAA) from CT data.
  • PGACM offers an improvement over existing active contour models for AAA segmentation.
  • This technique shows potential utility for clinical applications in patients with AAA.