From Aneurysms to Dissections: A Transfer Learning Approach for CTA Segmentation

The aim of this study is to develop an algorithm for automatic segmentation of aortic dissection in contrast-enhanced computed tomography images. A 2.5D architecture was employed to identify the true and false aortic lumen, major collateral vessels, stent-graft, and false lumen thrombosis in both pre- and post-operative conditions. To address the limited size of the dissection dataset, consisting of 82 scans, a transfer learning approach was applied using a model pre-trained on 336 scans from patients with aneurysmal disease. Preliminary results, evaluated in terms of Dice Score, demon-strate promising segmentation performance across the targeted structures. However, collection of further annotated data is necessary to enhance model stability and generalizability. In the future, these segmentations could be leveraged to automate medical image analysis, reducing manual effort and variability while improving the assessment and diagnosis of aortic diseases.