CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography
  Angiography via Context-Aware Shifted Window Self-Attention
Muhammad Imran Jonathan R Krebs Veera Rajasekhar Reddy Gopu Brian Fazzone Vishal Balaji Sivaraman Amarjeet Kumar Chelsea Viscardi Robert Evans Heithaus Benjamin Shickel Yuyin Zhou Michol A Cooper Wei Shao
Advancements in medical imaging and endovascular grafting have facilitated
minimally invasive treatments for aortic diseases. Accurate 3D segmentation of
the aorta and its branches is crucial for interventions, as inaccurate
segmentation can lead to erroneous surgical planning and endograft
construction. Previous methods simplified aortic segmentation as a binary image
segmentation problem, overlooking the necessity of distinguishing between
individual aortic branches. In this paper, we introduce Context Infused
Swin-UNet (CIS-UNet), a deep learning model designed for multi-class
segmentation of the aorta and thirteen aortic branches. Combining the strengths
of Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet adopts
a hierarchical encoder-decoder structure comprising a CNN encoder, symmetric
decoder, skip connections, and a novel Context-aware Shifted Window
Self-Attention (CSW-SA) as the bottleneck block. Notably, CSW-SA introduces a
unique utilization of the patch merging layer, distinct from conventional Swin
transformers. It efficiently condenses the feature map, providing a global
spatial context and enhancing performance when applied at the bottleneck layer,
offering superior computational efficiency and segmentation accuracy compared
to the Swin transformers. We trained our model on computed tomography (CT)
scans from 44 patients and tested it on 15 patients. CIS-UNet outperformed the
state-of-the-art SwinUNetR segmentation model, which is solely based on Swin
transformers, by achieving a superior mean Dice coefficient of 0.713 compared
to 0.697, and a mean surface distance of 2.78 mm compared to 3.39 mm.
CIS-UNet's superior 3D aortic segmentation offers improved precision and
optimization for planning endovascular treatments. Our dataset and code will be
publicly available.