Abstract：
OBJECTIVE: Aortic dissection (AD) is a serious condition requiring rapid and accurate diagnosis. In this study, we aimed to improve the diagnostic accuracy of AD by presenting a novel method for aortic segmentation in computed tomography images that uses a combination of a transformer and a UNet cascade network with a Zoom-Out and Zoom-In scheme (ZOZI-seg).
METHODS: The proposed method segments each compartment of the aorta, comprising the true lumen (TL), false lumen (FL), and thrombosis (TH) using a cascade strategy that captures both the global context (anatomical structure) and the local detail texture based on the dynamic patch size with ZOZI schemes. The ZOZI-seg model has a two-stage architecture using both a \"3D transformer for panoptic context-awareness\" and a \"3D UNet for localized texture refinement.\" The unique ZOZI strategies for patching were demonstrated in an ablation study. The performance of our proposed ZOZI-seg model was tested using a dataset from Asan Medical Center and compared with those of existing models such as nnUNet and nnFormer.
RESULTS: In terms of segmentation accuracy, our method yielded better results, with Dice similarity coefficients (DSCs) of 0.917, 0.882, and 0.630 for TL, FL, and TH, respectively. Furthermore, we indirectly compared our model with those in previous studies using an external dataset to evaluate its robustness and generalizability.
CONCLUSIONS: This approach may help in the diagnosis and treatment of AD in different clinical situations and provide a strong basis for further research and clinical applications.
METHODS: The proposed method segments each compartment of the aorta, comprising the true lumen (TL), false lumen (FL), and thrombosis (TH) using a cascade strategy that captures both the global context (anatomical structure) and the local detail texture based on the dynamic patch size with ZOZI schemes. The ZOZI-seg model has a two-stage architecture using both a \"3D transformer for panoptic context-awareness\" and a \"3D UNet for localized texture refinement.\" The unique ZOZI strategies for patching were demonstrated in an ablation study. The performance of our proposed ZOZI-seg model was tested using a dataset from Asan Medical Center and compared with those of existing models such as nnUNet and nnFormer.
RESULTS: In terms of segmentation accuracy, our method yielded better results, with Dice similarity coefficients (DSCs) of 0.917, 0.882, and 0.630 for TL, FL, and TH, respectively. Furthermore, we indirectly compared our model with those in previous studies using an external dataset to evaluate its robustness and generalizability.
CONCLUSIONS: This approach may help in the diagnosis and treatment of AD in different clinical situations and provide a strong basis for further research and clinical applications.
摘要：
目的：主动脉夹层（AD）是一种需要快速准确诊断的严重疾病。在这项研究中,我们旨在通过提出一种新颖的计算机断层扫描图像中的主动脉分割方法，该方法使用变压器和UNet级联网络的组合以及放大和放大方案（ZOZI-seg）来提高AD的诊断准确性。
方法：所提出的方法分割了主动脉的每个隔室，包括真腔(TL)，假腔(FL),和血栓形成（TH）使用级联策略，该策略基于ZOZI方案的动态补丁大小捕获全局上下文（解剖结构）和局部细节纹理。ZOZI-seg模型具有两级体系结构，同时使用“用于全景上下文感知的3D转换器”和用于局部纹理细化的3DUNet。“在消融研究中证明了独特的ZOZI修补策略。使用AsanMedicalCenter的数据集测试了我们提出的ZOZI-seg模型的性能，并将其与nnUNet和nnFormer等现有模型进行了比较。
结果：在分割准确性方面，我们的方法产生了更好的结果，TL的Dice相似系数(DSC)为0.917、0.882和0.630，FL,TH,分别。此外，我们使用外部数据集间接地将我们的模型与以前的研究进行了比较,以评估其稳健性和可泛化性.
结论：该方法可能有助于不同临床情况下AD的诊断和治疗，为进一步研究和临床应用提供有力依据。
方法：所提出的方法分割了主动脉的每个隔室，包括真腔(TL)，假腔(FL),和血栓形成（TH）使用级联策略，该策略基于ZOZI方案的动态补丁大小捕获全局上下文（解剖结构）和局部细节纹理。ZOZI-seg模型具有两级体系结构，同时使用“用于全景上下文感知的3D转换器”和用于局部纹理细化的3DUNet。“在消融研究中证明了独特的ZOZI修补策略。使用AsanMedicalCenter的数据集测试了我们提出的ZOZI-seg模型的性能，并将其与nnUNet和nnFormer等现有模型进行了比较。
结果：在分割准确性方面，我们的方法产生了更好的结果，TL的Dice相似系数(DSC)为0.917、0.882和0.630，FL,TH,分别。此外，我们使用外部数据集间接地将我们的模型与以前的研究进行了比较,以评估其稳健性和可泛化性.
结论：该方法可能有助于不同临床情况下AD的诊断和治疗，为进一步研究和临床应用提供有力依据。
