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Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion

Xuyang Zhang1, Guoliang Cheng1, Xiaofeng Han2

  • 1School of Medical Technology, Beijing Institute of Technology, Beijing, People's Republic of China.

Physics in Medicine and Biology
|September 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for segmenting postoperative type-b aortic dissection (AD) models. The new method enables rapid, accurate patient-specific 3D analyses for improved thoracic endovascular aortic repair (TEVAR) assessments.

Keywords:
deep learningglobal-local fusion learningimage segmentationpostoperative type-b aortic dissectionvolume quantification

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Area of Science:

  • Cardiovascular Imaging and Intervention
  • Artificial Intelligence in Medical Diagnostics
  • Computational Fluid Dynamics in Healthcare

Background:

  • Type-b aortic dissection (AD) is a critical condition requiring thoracic endovascular aortic repair (TEVAR).
  • Current clinical practice lacks rapid, accurate segmentation for patient-specific postoperative AD models.
  • This limitation hinders 3D morphological and hemodynamic analyses crucial for TEVAR assessment.

Purpose of the Study:

  • To develop a deep learning-based segmentation framework for postoperative type-b AD.
  • To enable the creation of patient-specific AD models for enhanced clinical assessment.
  • To facilitate 3D morphological and hemodynamic analyses in TEVAR planning and follow-up.

Main Methods:

  • A two-stage deep learning segmentation approach was employed.
  • Stage 1: Multi-class segmentation of aorta, thrombus (TH), and branch vessels (BV) from cropped image patches.
  • Stage 2: Extraction of true lumen (TL) and false lumen (FL) from a straightened whole aorta image, incorporating a global-local fusion mechanism to enhance TH and BV segmentation.

Main Results:

  • The framework achieved state-of-the-art Dice Similarity Coefficients (DSC): 0.962 (TL), 0.921 (FL), 0.811 (TH), and 0.884 (BV) on a multi-center dataset.
  • The global-local fusion mechanism significantly improved TH and BV segmentation DSC by 2.3% and 1.4%, respectively (p < 0.05).
  • The model demonstrated superior accuracy in vascular volume quantification, particularly for patients with enlarged TH+FL post-TEVAR, and showed good generalizability across different institutions.

Conclusions:

  • The developed deep learning framework provides rapid and accurate segmentation of postoperative type-b AD.
  • This enables the generation of patient-specific models for comprehensive 3D morphological and hemodynamic analyses.
  • The framework supports more quantitative and personalized TEVAR assessments, improving clinical decision-making.