Full Description
This book constitutes the proceedings of the First MICCAI Challenge Multi-class Segmentation of the Aorta, AortaSeg 2024, held in conjunction with MICCAI 2024, as a virtual event, during October 2024.
The 10 papers included in the book were carefully reviewed and selected from 16 submitting teams. This challenge aimed to advance the field of medical image segmentation by introducing the first large-scale, publicly available dataset for multi-class segmentation of the aorta, its branches, and clinically relevant zones in computed tomography angiography (CTA).
Contents
.- Multi-Class Segmentation of Aortic Branches and Zones in CTA
.- Coarse-to-Fine Aortic Segmentation on CTA Using a Two-Stage nnUNet-Based Framework.
.- Hierarchical Semantic Learning for Multi-Class Aorta Segmentation.
.- U-Net-Based Segmentation of Aortic Branches and Zones in CTA Scans.
.- Anatomically Guided Two-Stage 3D Aorta Segmentation in CT Angiography.
.- Combining Region-Based and Topological Losses in the nnU-Net Framework for Advanced Aorta Segmentation.
.- Data-Centric Multiclass Aortic Segmentation: Revisiting Classical Architectures in Low-Data Regimes.
.- AortaST: A Student-Teacher Framework for Multi-Class Aortic Segmentation.
.- Accurate and Efficient Multi-Class Segmentation for Aortic Branches and Zones in CTA.
.- Application of nnUNet for Multi-Class Segmentation of Aortic Branches and Zones in CTA.
.- A Mamba-Based Method with Gated Attention for Human Aorta Segmentation.
