Accurate landmark localization in 3D medical volumes is nowadays needed for many medical and technical applications such as segmentation, detection of anatomical structures, image registration, and more. Due to the time-consuming process of manual landmarking and the variety of landmark positioning by individual observers, a more uniform and automatic solution is needed. This paper examines a state-of-The-Art approach based on a patch-based Convolutional Neural Network (CNN), that is able to predict multiple landmarks simultaneously, and its performance on ill-posed landmarks. It combines regression and classification in one framework to improve the localization accuracy of individual landmarks by taking into consideration the relationship between an image patch and an anatomical landmark position. During inference, image patches are passed to the CNN as input, which are then moved by an amount, specified by the regression output, in the direction of highest probability, which is specified by the classification output. One of the main challenges here was that there are no salient landmarks in the aorta, they are all just generic points in a tube. This adds variability in the manual positioning of the landmarks and even the predictions of the network for each volume. The network's prediction accuracy was quantitatively evaluated and the best setup achieved an average landmark localization error of 16.04mm, with 99.0% of all predictions having a mean error smaller than 30 mm, while the best prediction during evaluation had a mean error of 3.53 mm over all landmarks.