Multi-Class Segmentation of Aortic Branches and Zones on Computed Tomography Angiography

The aorta, the body's largest artery, can face potential threats like dissection and aneurysm, requiring prompt surgical intervention. Traditional surgical techniques for aortic disease often carry significant risks. Recent advancements in medical imaging, particularly computed tomography angiography (CTA), and minimally invasive approaches like endovascular grafting, offer a promising alternative. Accurate 3D segmentation of the aorta and its branches and zones on CTA is crucial for successful interventions. Inaccurate segmentation can lead to critical errors in surgical planning and endograft design, jeopardizing patient safety and treatment outcomes.
 
While machine learning has revolutionized 3D medical image analysis, its potential in acute uncomplicated type B aortic dissection (auTBAD), the most common aortic emergency, remains largely unexplored. In the clinical realm, auTBAD is sub-categorized using SVS/STS zones, a detailed classification system defined by specific zones of the aorta in relation to aortic branches. Surgeons rely on this classification system to determine the optimum treatment algorithm for each patient. Current methods for aortic segmentation often treat it as a binary segmentation problem, neglecting the essential differentiation between individual aortic branches and their relationships to SVS/STS zones.
 
This challenge addresses these limitations by offering the first large-scale dataset of 100 CTA volumes paired with detailed annotations for 23 different aortic branches and the clinically relevant SVS/STS zones. Participating teams will have the opportunity to develop innovative algorithms for accurate, automated, and multi-class segmentation of this intricate vascular structure.
 
By fostering advancements in image analysis techniques for CTA, this challenge aims to:
(1) Improve clinical care for patients with aortic diseases by enabling accurate diagnosis, more precise surgical planning, and potentially safer, minimally invasive interventions.
(2) Bring greater attention and research focus to auTBAD, a relatively rare and challenging disease, potentially leading to novel treatment strategies.
(3) Bridge interdisciplinary communication between researchers in medical image analysis, computer vision, and machine learning, paving the way for collaborative solutions to overcome technical barriers in complex aortic segmentation tasks.
 
In summary, this challenge has three main features:
(1) Task: this is the first challenge for the segmentation of aortic branches and zones in CTA scans.
(2) Dataset: we provide the largest annotated dataset for aortic segmentation, including 100 3D CTA scans.
(3) Evaluation: we focus on both segmentation accuracy and segmentation efficiency.