Unlabeled Cross-Center Automatic Analysis for TAAD: An Integrated Framework from Segmentation to Clinical Features
Summary: arXiv:2603.26019v1 Announce Type: cross
Abstract
Type A Aortic Dissection (TAAD) is a life-threatening cardiovascular emergency that demands rapid and precise preoperative evaluation. While key anatomical and pathological features are decisive for surgical planning, current research focuses predominantly on improving segmentation accuracy, leaving the reliable, quantitative extraction of clinically actionable features largely under-explored. Furthermore, constructing comprehensive TAAD datasets requires labor-intensive, expert-level pixel-wise annotations, which is impractical for most clinical institutions. Due to significant domain shift, models trained on a single center dataset also suffer from severe performance degradation during cross-institutional deployment.
Introduction
This study addresses a clinically critical challenge: the accurate extraction of key TAAD clinical features during cross-institutional deployment in the total absence of target-domain annotations. To this end, we propose an unsupervised domain adaptation (UDA)-driven framework for the automated extraction of TAAD clinical features.
Framework Overview
The framework leverages limited source-domain labels while effectively adapting to unlabeled data from target domains. Tailored for real-world emergency workflows, our framework aims to achieve:
- Stable cross-institutional multi-class segmentation
- Reliable and quantifiable clinical feature extraction
- Practical deployability independent of high-cost annotations
Methodology
Extensive experiments demonstrate that our method significantly improves cross-domain segmentation performance compared to existing state-of-the-art approaches. Through rigorous testing, we have validated the framework’s capability to operate without the need for expert-level annotations, thus facilitating broader accessibility and applicability in various clinical settings.
Clinical Impact
More importantly, a reader study involving multiple cardiovascular surgeons confirms that the automatically extracted clinical features provide meaningful assistance for preoperative assessment. This highlights the practical utility of the proposed end-to-end segmentation-to-feature pipeline.
Conclusion
In conclusion, our integrated framework represents a significant advancement in the field of cardiovascular emergency care. By enabling the automated extraction of key clinical features in the absence of extensive annotations, it not only enhances surgical planning but also stands to improve patient outcomes in critical situations. Future work will focus on further refining the framework and exploring its applicability across other medical domains.