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Abdominal Aortic Thrombus Segmentation in Postoperative Computed Tomography Angiography Images Using Bi-Directional

Younhyun Jung1, Suhyeon Kim1, Jihu Kim1

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A new method improves thrombus segmentation in abdominal aortic aneurysm (AAA) computed tomography angiography (CTA) scans. This approach uses bi-directional convolutional long short-term memory (Bi-CLSTM) to analyze adjacent images for more accurate results.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Surgery

Background:

  • Abdominal aortic aneurysm (AAA) poses a significant mortality risk.
  • Computed tomography angiography (CTA) is crucial for postoperative AAA monitoring.
  • Accurate thrombus segmentation in CTA is vital for clinical decisions.

Purpose of the Study:

  • To develop an improved method for thrombus segmentation in postoperative AAA CTA images.
  • To leverage volumetric coherence from adjacent images for enhanced segmentation accuracy.
  • To overcome limitations of existing 2D segmentation methods.

Main Methods:

  • Proposed a novel thrombus ROI segmentation method utilizing spatial features and volumetric coherence.
  • Adopted a bi-directional convolutional long short-term memory (Bi-CLSTM) architecture.
  • Compared the Bi-CLSTM method against 2D and 3D segmentation alternatives on a dataset of 60 CTA volumes.

Main Results:

  • The Bi-CLSTM method demonstrated superior segmentation performance compared to existing techniques.
  • Achieved higher total overlap and lower false negative rates than the second-best method (2D U-net++).
  • The method effectively handles postoperative artifacts and noise by incorporating adjacent image data.

Conclusions:

  • The proposed Bi-CLSTM-based method offers a more robust and accurate approach for thrombus segmentation in AAA CTA.
  • This advancement can aid in quantitative assessment and clinical decision-making for AAA patients.
  • Volumetric coherence learning is a promising strategy for improving medical image segmentation.