目的:A型主动脉夹层(TAAD)是一种危及生命的主动脉疾病。撕裂涉及升主动脉,并进入主动脉壁各层的分离和假腔的出现。TAAD的准确分割可以为疾病评估提供帮助,指导临床治疗。
方法:本研究采用nnU-Net,一个国家的最先进的生物医学分割网络架构,分割对比增强CT图像并量化TAAD的形态特征。CT数据集来自24例TAAD患者。CT图像的手动分割和注释被用作地面实况。利用二维(2D)nnU-Net和三维(3D)nnU-Net架构以及基于Dicle和交叉熵的损失函数来分割真腔(TL)。假腔(FL),和图像上的内膜瓣。进行了四次交叉验证以评估两种nnU-Net架构的性能。六个指标,包括准确性,精度,召回,联合路口,骰子相似系数(DSC),和Hausdorff距离,进行了计算,以评估TAAD数据集中2D和3DnnU-Net算法的性能。基于分割结果对来自2D和3DnnU-Net算法的主动脉形态特征进行量化并进行比较。
结果:总体而言,3DnnU-Net架构在TAADCT数据集中具有更好的性能,TL和FL分割精度高达99.9%。基于3DnnU-Net的TL和FL的DSC分别为88.42%和87.10%。对于主动脉TL和FL直径,从3DnnU-Net体系结构的分割结果计算的FL面积具有较小的相对误差(3.89-6.80%),与2DnnU-Net架构相比(相对误差:4.35-9.48%)。
结论:nnU-Net架构可以作为TAAD自动分割和量化的基础,这可以帮助快速诊断,手术计划,以及随后的主动脉生物力学模拟。
OBJECTIVE: Type A aortic dissection (TAAD) is a life-threatening aortic disease. The tear involves the ascending aorta and progresses into the separation of the layers of the aortic wall and the occurrence of a false lumen. Accurate segmentation of TAAD could provide assistance for disease assessment and guidance for clinical treatment.
METHODS: This study applied nnU-Net, a state-of-the-art biomedical segmentation network architecture, to segment contrast-enhanced CT images and quantify the morphological features for TAAD. CT datasets were acquired from 24 patients with TAAD. Manual segmentation and annotation of the CT images was used as the ground-truth. Two-dimensional (2D) nnU-Net and three-dimensional (3D) nnU-Net architectures with Dice- and cross entropy-based loss functions were utilized to segment the true lumen (TL), false lumen (FL), and intimal flap on the images. Four-fold cross validation was performed to evaluate the performance of the two nnU-Net architectures. Six metrics, including accuracy, precision, recall, Intersection of Union, Dice similarity coefficient (DSC), and Hausdorff distance, were calculated to evaluate the performance of the 2D and 3D nnU-Net algorithms in TAAD datasets. Aortic morphological features from both 2D and 3D nnU-Net algorithms were quantified based on the segmented results and compared.
RESULTS: Overall, 3D nnU-Net architectures had better performance in TAAD CT datasets, with TL and FL segmentation accuracy up to 99.9%. The DSCs of TLs and FLs based on the 3D nnU-Net were 88.42% and 87.10%. For the aortic TL and FL diameters, the FL area calculated from the segmentation results of the 3D nnU-Net architecture had smaller relative errors (3.89-6.80%), compared to the 2D nnU-Net architecture (relative errors: 4.35-9.48%).
CONCLUSIONS: The nnU-Net architectures may serve as a basis for automatic segmentation and quantification of TAAD, which could aid in rapid diagnosis, surgical planning, and subsequent biomechanical simulation of the aorta.