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• The proposed framework achieves significantly improved cross-domain segmentation performance for True Lumen (TL), False Lumen (FL), and Intimal Flap (IF) compared to state-of-the-art unsupervised domain adaptation approaches.result 95%
• The framework enables reliable and quantifiable extraction of clinical features such as True Lumen Circumference (TLC), False Lumen Area Ratio (FLAR), and Branch Vessel Involvement (BVI) for Type A Aortic Dissection (TAAD) preoperative evaluation.method 90%
• The automatically extracted clinical features provide meaningful assistance for preoperative assessment, as confirmed by a reader study involving cardiovascular surgeons.result 90%
• The proposed framework operates in the total absence of target-domain annotations, addressing the impracticality of labor-intensive, expert-level pixel-wise annotations for TAAD datasets.method 95%
• The framework is designed for practical deployability, independent of high-cost annotations, making it suitable for real-world emergency workflows.technical 90%
• The framework utilizes unsupervised domain adaptation (UDA) to effectively adapt to unlabeled data from target domains while leveraging limited source-domain labels.method 95%
• Existing TAAD research predominantly focuses on improving segmentation accuracy, leaving the reliable, quantitative extraction of clinically actionable features under-explored.limitation 90%
• The proposed framework achieves significantly improved cross-domain segmentation performance for True Lumen (TL), False Lumen (FL), and Intimal Flap (IF) compared to state-of-the-art unsupervised domain adaptation approaches.result 95%
• The framework enables reliable and quantifiable extraction of clinical features such as True Lumen Circumference (TLC), False Lumen Area Ratio (FLAR), and Branch Vessel Involvement (BVI) for Type A Aortic Dissection (TAAD) preoperative evaluation.method 90%
• The automatically extracted clinical features provide meaningful assistance for preoperative assessment, as confirmed by a reader study involving cardiovascular surgeons.result 95%
• The proposed framework is designed to operate in the total absence of target-domain annotations, addressing the impracticality of labor-intensive expert annotations for TAAD datasets.method 95%
• The framework utilizes unsupervised domain adaptation (UDA) to effectively adapt models trained on a source domain to unlabeled target domains, mitigating performance degradation due to domain shift.method 95%

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