Cardiovascular diseases are one of the strongest burdens in healthcare. If misdiagnosed, they can lead to life-threatening complications. This is especially true for aortic dissections, which may require immediate surgery depending on the categorization and still lead to late adverse events. Aortic dissection occurs when the aortic duct splits into two blood streams, the true and false lumina. The morphological characteristics of the aorta are therefore crucial for a clinician and provide vital support since they can be used to extract significant information for surgery and treatment planning. In this work, we revive a successful modeling technique – convolution surfaces – to model the lumina in aortic dissections. The skeleton of the lumina and local radial information are used to represent the true and the false lumen through convolution of local segments. Additionally, we introduce an optimization strategy based on a genetic algorithm to create the separation caused by the dissection flap.
Aortic dissection is an acute condition of the aorta. It typically starts with an intimal tear and continues with the separation of the aortic wall layers. This situation typically leads to the creation of a second lumen, i.e., the false lumen, where blood can flow into. For diagnosis of this pathology, computed tomography angiography (CTA) is usually used. To have a better understanding of its causes and for measuring cross-sectional caliber at onset and at each follow-up, segmentation of true and false lumen is important in clinical use. In this work, a pipeline for aortic dissection segmentation is evaluated to obtain the correct visualization of true and false lumen separated by the dissection flap that characterizes this pathology. We provide an evaluation of three different vessel enhancement filters, used as a preprocessing step, through both a qualitative and quantitative evaluation.
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