Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D
  ResUNet: Contribution to the SEG.A Challenge
Marek Wodzinski Henning Müller
Automatic aorta segmentation from 3-D medical volumes is an important yet
difficult task. Several factors make the problem challenging, e.g. the
possibility of aortic dissection or the difficulty with segmenting and
annotating the small branches. This work presents a contribution by the MedGIFT
team to the SEG.A challenge organized during the MICCAI 2023 conference. We
propose a fully automated algorithm based on deep encoder-decoder architecture.
The main assumption behind our work is that data preprocessing and augmentation
are much more important than the deep architecture, especially in low data
regimes. Therefore, the solution is based on a variant of traditional
convolutional U-Net. The proposed solution achieved a Dice score above 0.9 for
all testing cases with the highest stability among all participants. The method
scored 1st, 4th, and 3rd in terms of the clinical evaluation, quantitative
results, and volumetric meshing quality, respectively. We freely release the
source code, pretrained model, and provide access to the algorithm on the
Grand-Challenge platform.