Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Ann Surg. 2022 Dec 1;276(6):e1017-lpagee1027. doi: 10.1097/SLA.0000000000004595. Epub 2020 Nov 23.
Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be present that prevent accurate reconstruction. In aortic aneurysmal disease, a thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in >90% of cases. These deformations prevent the automatic extraction of vital clinical information by existing image reconstruction methods.
In this study, a deep learning architecture consisting of a modified U-Net with attention-gating was implemented to establish a high-throughput and automated segmentation pipeline of pathological blood vessels in CT images acquired with or without the use of a contrast agent.
Seventy-Five patients with paired noncontrast and contrast-enhanced CT images were randomly selected from an ongoing study (Ethics Ref 13/SC/0250), manually annotated and used for model training and evaluation. Data augmentation was implemented to diversify the training data set in a ratio of 10:1. The performance of our Attention-based U-Net in extracting both the inner (blood flow) lumen and the wall structure of the aortic aneurysm from CT angiograms was compared against a generic 3-D U-Net and displayed superior results. Implementation of this network within the aortic segmentation pipeline for both contrast and noncontrast CT images has allowed for accurate and efficient extraction of the morphological and pathological features of the entire aortic volume.
This extraction method can be used to standardize aneurysmal disease management and sets the foundation for complex geometric and morphological analysis. Furthermore, this pipeline can be extended to other vascular pathologies.
现有的从计算机断层扫描（CT）血管造影重建血管结构的方法依赖于对比剂注射来增强血管腔内的放射密度。然而，可能存在血管的病理性变化，从而导致重建不准确。在主动脉瘤疾病中，在扩张的动脉瘤囊内附着于主动脉壁的血栓在>90%的病例中存在。这些变形阻止了现有图像重建方法自动提取重要的临床信息。
在这项研究中，实现了一个由带有注意力门控的修改后的 U-Net 组成的深度学习架构，以建立一个在使用或不使用造影剂的 CT 图像中对病理性血管进行高通量和自动化分割的流水线。
从一项正在进行的研究（伦理参考 13/SC/0250）中随机选择了 75 例具有配对的非增强和增强 CT 图像的患者，对其进行手动注释并用于模型训练和评估。实施了数据扩充，以 10:1 的比例使训练数据集多样化。我们的基于注意力的 U-Net 在从 CT 血管造影中提取主动脉瘤的内部（血流）管腔和壁结构方面的性能与通用的 3-D U-Net 进行了比较，显示出更好的结果。在对比和非对比 CT 图像的主动脉分割流水线中实施该网络，允许准确高效地提取整个主动脉体积的形态和病理特征。
这种提取方法可用于标准化动脉瘤疾病的管理，并为复杂的几何和形态分析奠定基础。此外，该流水线可以扩展到其他血管病变。
