Abnormal aortic wall thickness and the presence of aortic plaque have been linked to various types of cardiovascular disease. QuantiĄcation of both indicators currently depends on manual or semi-automatic methods which suffer from limited quality and long acquisition times. This work presents various fully automatic state-of-the art solutions to two medical image processing problems: aortic wall segmentation and plaque slice detection. A u-net derived residual convolutional neural network (CNN), a cascaded pipeline of two CNNs and a 3D CNN architecture are used for aortic wall segmentation. Plaque detection is performed by a standard multilayer residual CNN classification architecture, a u-net derived CNN classifier and a capsule CNN. The experiments show that the u-net inspired residual CNN performs best at the aortic wall segmentation task with a Dice score of around 0.8 while the capsule CNN achieves the best results in slice-wise plaque detection with a precision of 0.74 and an accuracy of 0.68.