HVPUNet: an automated deep learning model for precise true–false lumen segmentation in aortic dissection

Key Points

• The model achieves precise lumen segmentation in cases of aortic dissection, improving diagnostic capabilities.
• An accuracy rate of 92% was reached for true-false lumen segmentation, enhancing clinical evaluation of patients.
• Observational analysis employing deep learning showcased the model's efficiency across various imaging datasets.
• The findings may enable earlier diagnosis and treatment strategies in aortic dissection scenarios.