  Medical image segmentation often requires segmenting multiple elliptical
objects on a single image. This includes, among other tasks, segmenting vessels
such as the aorta in axial CTA slices. In this paper, we present a general
approach to improving the semantic segmentation performance of neural networks
in these tasks and validate our approach on the task of aorta segmentation. We
use a cascade of two neural networks, where one performs a rough segmentation
based on the U-Net architecture and the other performs the final segmentation
on polar image transformations of the input. Connected component analysis of
the rough segmentation is used to construct the polar transformations, and
predictions on multiple transformations of the same image are fused using
hysteresis thresholding. We show that this method improves aorta segmentation
performance without requiring complex neural network architectures. In
addition, we show that our approach improves robustness and pixel-level recall
while achieving segmentation performance in line with the state of the art.
