Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge
This work addresses aorta segmentation for medical imaging applications, but it is incremental as it applies an existing automated method to a specific challenge.
The paper tackled the problem of segmenting the aorta from 3D CT scans for early disease detection, achieving a first-place ranking in the SEG.A. 2023 challenge with an average Dice score of 0.920 and HD95 of 6.013.
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.