PDF(Pubmed)
Abstract：
UNASSIGNED: Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans.
UNASSIGNED: We introduce an automated DL-based approach that leverages anatomical information from the lung\'s vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net.
UNASSIGNED: Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model\'s generalization capabilities. Finally, the method\'s robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases.
UNASSIGNED: Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.
UNASSIGNED: We introduce an automated DL-based approach that leverages anatomical information from the lung\'s vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net.
UNASSIGNED: Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model\'s generalization capabilities. Finally, the method\'s robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases.
UNASSIGNED: Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.
摘要：
■我们的研究调查了将先前的解剖学知识纳入深度学习（DL）方法的潜在好处，该方法设计用于在胸部CT扫描中自动分割肺叶。
■我们介绍了一种基于DL的自动化方法，该方法利用来自肺部血管系统的解剖信息来指导和增强分割过程。这涉及利用肺血管连通性(LVC)图，编码相关肺血管解剖数据。我们的研究探讨了nnU-Net框架内三种不同神经网络架构的性能：独立的U-Net，多任务U-Net，和级联U网。
■实验结果表明，在DL模型中包含LVC信息可以提高分割精度，特别是,在具有挑战性的呼气胸部CT容积边界区域。此外，我们的研究证明了LVC增强模型泛化能力的潜力。最后,通过对10例COVID-19患者的肺叶分割，评估了该方法的鲁棒性，证明了其在肺部疾病中的适用性。
■结合先前的解剖信息，例如LVC，进入DL模型显示出增强细分性能的希望，特别是在边界区域。然而,这种改进的程度有局限性，进一步探索其实际适用性。
■我们介绍了一种基于DL的自动化方法，该方法利用来自肺部血管系统的解剖信息来指导和增强分割过程。这涉及利用肺血管连通性(LVC)图，编码相关肺血管解剖数据。我们的研究探讨了nnU-Net框架内三种不同神经网络架构的性能：独立的U-Net，多任务U-Net，和级联U网。
■实验结果表明，在DL模型中包含LVC信息可以提高分割精度，特别是,在具有挑战性的呼气胸部CT容积边界区域。此外，我们的研究证明了LVC增强模型泛化能力的潜力。最后,通过对10例COVID-19患者的肺叶分割，评估了该方法的鲁棒性，证明了其在肺部疾病中的适用性。
■结合先前的解剖信息，例如LVC，进入DL模型显示出增强细分性能的希望，特别是在边界区域。然而,这种改进的程度有局限性，进一步探索其实际适用性。
