Aortic aneurysms (AAs) are localized dilations of the aortic wall, prone to rupture with expansion, an often deadly event (Chaikof et al., 2018). The decision to intervene upon an AA is based primarily upon standardized size criteria and rate of expansion over time as determined radiographically. Early detection and surveillance are essential to timely intervention. This study proposes an end-to-end deep learning algorithm for sequential slice-by-slice segmentation of AAs in computed tomography angiography (CTA). The algorithm uses predicted masks to compute the size of AAs through contour area measurements. The goal of sequential quantification of AA sizes in a series of single-slice CTA images is to identify the largest aorta slice when measuring the maximum diameter. Current manual methods of inspection have led to large amounts of intra-operator variability in measuring maximum aortic diameter among radiologists (Cayne et al., 2004). Image data for training the algorithm was acquired in an unconventional web-sourced manner from open case studies and preprints.