Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections

• URL: http://arxiv.org/abs/2506.22222v1
• Date: Fri, 27 Jun 2025 13:38:33 GMT
• Title: Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections
• Authors: Hao Xu, Ruth Lim, Brian E. Chapman,
• Abstract summary: We developed four deep learning-based pipelines for Type B aortic dissection segmentation.<n>Our approach achieved superior segmentation accuracy, with Dice Coefficients of 0.91 $pm$ 0.07 for TL, 0.88 $pm$ 0.18 for FL, and 0.47 $pm$ 0.25 for.
• Score: 4.545298205355719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Aortic dissections are life-threatening cardiovascular conditions requiring accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombosis (FLT) from CTA images for effective management. Manual segmentation is time-consuming and variable, necessitating automated solutions. Materials and Methods: We developed four deep learning-based pipelines for Type B aortic dissection segmentation: a single-step model, a sequential model, a sequential multi-task model, and an ensemble model, utilizing 3D U-Net and Swin-UnetR architectures. A dataset of 100 retrospective CTA images was split into training (n=80), validation (n=10), and testing (n=10). Performance was assessed using the Dice Coefficient and Hausdorff Distance. Results: Our approach achieved superior segmentation accuracy, with Dice Coefficients of 0.91 $\pm$ 0.07 for TL, 0.88 $\pm$ 0.18 for FL, and 0.47 $\pm$ 0.25 for FLT, outperforming Yao et al. (1), who reported 0.78 $\pm$ 0.20, 0.68 $\pm$ 0.18, and 0.25 $\pm$ 0.31, respectively. Conclusion: The proposed pipelines provide accurate segmentation of TBAD features, enabling derivation of morphological parameters for surveillance and treatment planning

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