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.

翻译：从三维医学体数据中自动分割主动脉是一项重要但困难的任务。多种因素使其具有挑战性，例如主动脉夹层的可能性，以及小型分支分割和标注的难度。本文介绍了MedGIFT团队对MICCAI 2023会议期间组织的SEG.A挑战的贡献。我们提出了一种基于深度编码器-解码器架构的全自动算法。我们工作的主要假设是，数据预处理和增强比深度架构本身重要得多，尤其是在数据量有限的场景下。因此，该解决方案基于传统卷积U-Net的一个变体。所提出的方案在所有测试案例中均获得了超过0.9的Dice分数，并且在所有参与者中稳定性最高。该方法在临床评估、定量结果和体积网格质量方面分别排名第一、第四和第三。我们免费发布源代码、预训练模型，并在Grand-Challenge平台上提供算法的访问权限。