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.

在本文中，我们介绍了一种用于主动脉和其十三个分支的多类分割的深度学习模型 CIS-UNet，并证明其在 CT 扫描中优于现有的基于 Swin 转换器的模型，提供更高的精度和优化，为心血管内治疗的规划提供改进。