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.

Aortic dissection (AD) is a life-threatening cardiovascular disease with a high mortality rate. The accurate and generalized 3-D reconstruction of AD from CT-angiography can effectively assist clinical procedures and surgery plans, however, is clinically unavaliable due to the lacking of efficient tools. In this study, we presented a novel multi-stage segmentation framework for type B AD to extract true lumen (TL), false lumen (FL) and all branches (BR) as different classes. Two cascaded neural networks were used to segment the aortic trunk and branches and to separate the dual lumen, respectively. An aortic straightening method was designed based on the prior vascular anatomy of AD, simplifying the curved aortic shape before the second network. The straightening-based method achieved the mean Dice scores of 0.96, 0.95 and 0.89 for TL, FL, and BR on a multi-center dataset involving 120 patients, outperforming the end-to-end multi-class methods and the multi-stage methods without straightening on the dual-lumen segmentation, even using different network architectures. Both the global volumetric features of the aorta and the local characteristics of the primary tear could be better identified and quantified based on the straightening. Comparing to previous deep learning methods dealing with AD segmentations, the proposed framework presented advantages in segmentation accuracy.

To compare the diagnostic values of high-resolution magnetic resonance (HR-MRI) with computed tomographic angiography (CTA) in young adults with ischemic stroke due to cervical artery dissections. Totally 42 symptomatic patients were recruited in this study. All the 42 patients underwent both HR-MRI and CTA, including 28 patients with dissections confirmed by Digital Subtraction Angiography (DSA) and 4 patients with vertebral artery dissections diagnosed by follow-up. CTA and HR-MRI images were separately and blindly analyzed by two radiologists. The sensitivity, specificity, positive and negative predictive value of HR-MRI and CTA were calculated. The receiver operating characteristic (ROC) curves and AUC of each imaging modality were generated. A total of 20 carotid artery dissections, 12 vertebral artery dissections and 10 non-dissected cervical arteries were involved. The inter-observer concordance of HR-MRI and CTA was good (κ = 0.806 vs. 0.776). The sensitivity and specificity of HR-MRI and CTA on detecting the dissections were 87.5% vs. 62.5%, and 90.0% vs. 80.0%, respectively. Area under the ROC curve of HR-MRI [0.94 (95% CI 0.86-0.97)] was greater than that of CTA [0.87 (95% CI 0.71-1.0)]. Compared to CTA, HR-MRI is more sensitive and specific for the diagnosis of cervical artery dissections in high-risk symptomatic patients. This study supports the value of HR-MRI in non-invasive diagnosis of young adults with cervical artery dissections.

暂无摘要。