Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge
This work addresses the difficult task of aorta segmentation for medical imaging applications, but it is incremental as it builds on existing U-Net architectures with enhanced preprocessing and augmentation.
The authors tackled automatic aorta segmentation from 3-D medical volumes, achieving a Dice score above 0.9 for all testing cases and ranking 1st in clinical evaluation, 4th in quantitative results, and 3rd in volumetric meshing quality in the SEG.A challenge.
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.