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

TL;DR

This paper presents a top-performing automated 3D CT aorta segmentation method that achieved the highest accuracy in the MICCAI SEG.A. 2023 challenge, aiding early detection and monitoring of aortic diseases.

Contribution

The authors applied the Auto3DSeg method from MONAI to achieve state-of-the-art segmentation performance in a competitive challenge.

Findings

Achieved an average Dice score of 0.920

Ranked first in the SEG.A. 2023 challenge

Hausdorff Distance (HD95) of 6.013

Abstract

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.