Sequential vs. batch machine-learning with evolutionary hyperparameter optimization for segmenting aortic dissection thrombus

While delineation of aortic aneurysms has been subject of research in several publications, this represents the first contribution to address segmentation of thrombus in case of aortic dissections. The segmentation process ensues in multiplanar reformated slices (MPRs). In 3D CTA data, thrombus hardly differs from surrounding tissue outside the aorta. Segmentation is further complicated by the high variance of adjacent structures along the aorta in thoracic and abdominal area. Therefore, we propose a combination of machine learning methods and additional features for the detection of the aortic outer wall, which includes both lumen and thrombus. The optimal path is sought in each MPR in polar space based on the result of a classifier, as well as the filter response of a phase congruency filter and a distance-based component. Hyperparameters for the classifier are inferred by employing evolutionary algorithms.