目的:主动脉夹层(AD)患者的假腔血栓是确定主动脉重塑的有意义的指标,但在临床上难以测量。在这项研究中,提出了一种新的基于深度学习的分割策略,用于自动提取AD患者术后CT血管造影(CTA)图像中的假腔内血栓,提供了一种高效、便捷、高精度的分割方法。
方法:提出了一种两步分割策略。每个步骤都包含一个卷积神经网络(CNN)来分割主动脉和血栓,分别。第一步,使用CNN获得整个主动脉的二值分割掩模。第二步,另一个CNN被引入来分割血栓。第一步的结果用作第二步的额外输入,以突出显示复杂背景中的主动脉。此外,第二步设计并添加了跳过连接注意力细化模块,以通过有效利用低级特征来提高血栓细节的分割准确性.
结果:提出的方法提供了准确的血栓分割结果(骰子得分0.903±0.062,在Jaccard指数中0.828±0.092,和2.209±2.945在95%豪斯多夫距离),与没有先验信息的方法(骰子得分0.846±0.085)和没有跳过连接注意力细化的方法(骰子得分0.899±0.060)相比,显示出改善。此外,所提出的方法在真管腔和专利假管腔分割的骰子得分方面达到0.967±0.029和0.948±0.041,分别,表明所提出的方法在整个主动脉的分割任务中的优越性。
结论:提出了一种新颖的基于CNN的分割框架,以自动获得术后CTA图像中血栓形成的AD的血栓分割,这为血栓相关指标在临床和研究应用中的进一步应用提供了有用的工具。本文受版权保护。保留所有权利。
OBJECTIVE: The thrombus in the false lumen (FL) of aortic dissection (AD) patients is a meaningful indicator to determine aortic remodeling but difficult to measure in clinic. In this study, a novel segmentation strategy based on deep learning was proposed to automatically extract the thrombus in the FL in post-operative computed tomography angiography (CTA) images of AD patients, which provided an efficient and convenient segmentation method with high accuracy.
METHODS: A two-step segmentation strategy was proposed. Each step contained a convolutional neural network (CNN) to segment the aorta and the thrombus, respectively. In the first step, a CNN was used to obtain the binary segmentation mask of the whole aorta. In the second step, another CNN was introduced to segment the thrombus. The results of the first step were used as additional input to the second step to highlight the aorta in the complex background. Moreover, skip connection attention refinement (SAR) modules were designed and added in the second step to improve the segmentation accuracy of the thrombus details by efficiently using the low-level features.
RESULTS: The proposed method provided accurate thrombus segmentation results (0.903 ± 0.062 in dice score, 0.828 ± 0.092 in Jaccard index, and 2.209 ± 2.945 in 95% Hausdorff distance), which showed improvement compared to the methods without prior information (0.846 ± 0.085 in dice score) and the method without SAR (0.899 ± 0.060 in dice score). Moreover, the proposed method achieved 0.967 ± 0.029 and 0.948 ± 0.041 in dice score of true lumen (TL) and patent FL (PFL) segmentation, respectively, indicating the excellence of the proposed method in the segmentation task of the overall aorta.
CONCLUSIONS: A novel CNN-based segmentation framework was proposed to automatically obtain thrombus segmentation for thrombosed AD in post-operative CTA images, which provided a useful tool for further application of thrombus-related indicators in clinical and research application.