# Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge

arXiv: https://arxiv.org/abs/2310.04114 · HTML: https://mlsanity.com/paper/2310.04114/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo](https://mlsanity.com/paper/2108.02998/) (2021)

## Built on by (most cited first, 5 of 5)

- [VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging](https://mlsanity.com/paper/2406.05285/) (2024)
- [ENSAM: an efficient foundation model for interactive segmentation of 3D medical images](https://mlsanity.com/paper/2509.15874/) (2025)
- [Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge](https://mlsanity.com/paper/2510.24009/) (2025)
- [AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation](https://mlsanity.com/paper/2507.13404/) (2025)
- [Sketch2CT: Multimodal Diffusion for Structure-Aware 3D Medical Volume Generation](https://mlsanity.com/paper/2603.22509/) (2026)

## Abstract (arXiv)

Aorta provides the main blood supply of the body. Screening of aorta with imaging helps for early aortic disease detection and monitoring. In this work, we describe our solution to the Segmentation of the Aorta (SEG.A.231) from 3D CT challenge. We use automated segmentation method Auto3DSeg available in MONAI. Our solution achieves an average Dice score of 0.920 and 95th percentile of the Hausdorff Distance (HD95) of 6.013, which ranks first and wins the SEG.A. 2023 challenge.