PDF(Pubmed)
Abstract：
BACKGROUND: Endovascular thrombectomy (EVT) duration is an important predictor for neurological outcome. Recently it was shown that an angle of ≤ 90° of the internal carotid artery (ICA) is predictive for longer EVT duration. As manual angle measurement is not trivial and time-consuming, deep learning (DL) could help identifying difficult EVT cases in advance.
METHODS: We included 379 CT angiographies (CTA) of patients who underwent EVT between January 2016 and December 2020. Manual segmentation of 121 CTAs was performed for the aortic arch, common carotid artery (CCA) and ICA. These were used to train a nnUNet. The remaining 258 CTAs were segmented using the trained nnUNet with manual verification afterwards. Angles of left and right ICAs were measured resulting in two classes: acute angle ≤ 90° and > 90°. The segmentations together with angle measurements were used to train a convolutional neural network (CNN) determining the ICA angle. The performance was evaluated using Dice scores. The classification was evaluated using AUC and accuracy. Associations of ICA angle and procedural times was explored using median and Whitney‑U test.
RESULTS: Median EVT duration for cases with ICA angle > 90° was 48 min and with ≤ 90° was 64 min (p = 0.001). Segmentation evaluation showed Dice scores of 0.94 for the aorta and 0.86 for CCA/ICA, respectively. Evaluation of ICA angle determination resulted in an AUC of 0.92 and accuracy of 0.85.
CONCLUSIONS: The association between ICA angle and EVT duration could be verified and a DL-based method for semi-automatic assessment with the potential for full automation was developed. More anatomical features of interest could be examined in a similar fashion.
METHODS: We included 379 CT angiographies (CTA) of patients who underwent EVT between January 2016 and December 2020. Manual segmentation of 121 CTAs was performed for the aortic arch, common carotid artery (CCA) and ICA. These were used to train a nnUNet. The remaining 258 CTAs were segmented using the trained nnUNet with manual verification afterwards. Angles of left and right ICAs were measured resulting in two classes: acute angle ≤ 90° and > 90°. The segmentations together with angle measurements were used to train a convolutional neural network (CNN) determining the ICA angle. The performance was evaluated using Dice scores. The classification was evaluated using AUC and accuracy. Associations of ICA angle and procedural times was explored using median and Whitney‑U test.
RESULTS: Median EVT duration for cases with ICA angle > 90° was 48 min and with ≤ 90° was 64 min (p = 0.001). Segmentation evaluation showed Dice scores of 0.94 for the aorta and 0.86 for CCA/ICA, respectively. Evaluation of ICA angle determination resulted in an AUC of 0.92 and accuracy of 0.85.
CONCLUSIONS: The association between ICA angle and EVT duration could be verified and a DL-based method for semi-automatic assessment with the potential for full automation was developed. More anatomical features of interest could be examined in a similar fashion.
摘要：
背景：血管内血栓切除术（EVT）持续时间是神经系统预后的重要预测指标。最近表明，颈内动脉（ICA）的角度≤90°可以预测更长的EVT持续时间。由于手动角度测量并不简单且耗时，深度学习(DL)可以帮助提前识别困难的EVT病例。
方法：我们纳入了2016年1月至2020年12月期间接受EVT患者的379例CT血管造影（CTA）。人工分割主动脉弓121个CTA,颈总动脉（CCA）和ICA。这些被用来训练nnUNet。其余258个CTA使用经过训练的nnUNet进行分段，然后进行手动验证。测量左和右ICA的角度,得到两类:锐角≤90°和>90°。分割与角度测量一起用于训练确定ICA角度的卷积神经网络(CNN)。使用Dice评分评估性能。使用AUC和准确性评估分类。使用中位数和Whitney‑U检验探索了ICA角度和程序时间的关联。
结果：对于ICA角度>90°的病例，EVT的中位持续时间为48分钟，≤90°的病例为64分钟（p=0.001）。分割评估显示，主动脉的Dice评分为0.94，CCA/ICA的Dice评分为0.86，分别。ICA角度测定的评估导致AUC为0.92和准确度为0.85。
结论：可以验证ICA角度与EVT持续时间之间的关联，并开发了一种基于DL的半自动评估方法，具有完全自动化的潜力。可以以类似的方式检查更多感兴趣的解剖特征。
方法：我们纳入了2016年1月至2020年12月期间接受EVT患者的379例CT血管造影（CTA）。人工分割主动脉弓121个CTA,颈总动脉（CCA）和ICA。这些被用来训练nnUNet。其余258个CTA使用经过训练的nnUNet进行分段，然后进行手动验证。测量左和右ICA的角度,得到两类:锐角≤90°和>90°。分割与角度测量一起用于训练确定ICA角度的卷积神经网络(CNN)。使用Dice评分评估性能。使用AUC和准确性评估分类。使用中位数和Whitney‑U检验探索了ICA角度和程序时间的关联。
结果：对于ICA角度>90°的病例，EVT的中位持续时间为48分钟，≤90°的病例为64分钟（p=0.001）。分割评估显示，主动脉的Dice评分为0.94，CCA/ICA的Dice评分为0.86，分别。ICA角度测定的评估导致AUC为0.92和准确度为0.85。
结论：可以验证ICA角度与EVT持续时间之间的关联，并开发了一种基于DL的半自动评估方法，具有完全自动化的潜力。可以以类似的方式检查更多感兴趣的解剖特征。
