Abstract：
Non-contrast chest CT is widely used for lung cancer screening, and its images carry potential information of the thoracic aorta. The morphological assessment of the thoracic aorta may have potential value in the presymptomatic detection of thoracic aortic-related diseases and the risk prediction of future adverse events. However, due to low vasculature contrast in such images, visual assessment of aortic morphology is challenging and highly depends on physicians\' experience.
The main objective of this study is to propose a novel multi-task framework based on deep learning for simultaneous aortic segmentation and localization of key landmarks on unenhanced chest CT. The secondary objective is to use the algorithm to measure quantitative features of thoracic aorta morphology.
The proposed network is composed of two subnets to carry out segmentation and landmark detection, respectively. The segmentation subnet aims to demarcate the aortic sinuses of the Valsalva, aortic trunk and aortic branches, whereas the detection subnet is devised to locate five landmarks on the aorta to facilitate morphology measures. The networks share a common encoder and run decoders in parallel, taking full advantage of the synergy of the segmentation and landmark detection tasks. Furthermore, the volume of interest (VOI) module and the squeeze-and-excitation (SE) block with attention mechanisms are incorporated to further boost the capability of feature learning.
Benefiting from the multitask framework, we achieved a mean Dice score of 0.95, average symmetric surface distance of 0.53 mm, Hausdorff distance of 2.13 mm for aortic segmentation, and mean square error (MSE) of 3.23 mm for landmark localization in 40 testing cases.
We proposed a multitask learning framework which can perform segmentation of the thoracic aorta and localization of landmarks simultaneously and achieved good results. It can support quantitative measurement of aortic morphology for further analysis of aortic diseases, such as hypertension.
The main objective of this study is to propose a novel multi-task framework based on deep learning for simultaneous aortic segmentation and localization of key landmarks on unenhanced chest CT. The secondary objective is to use the algorithm to measure quantitative features of thoracic aorta morphology.
The proposed network is composed of two subnets to carry out segmentation and landmark detection, respectively. The segmentation subnet aims to demarcate the aortic sinuses of the Valsalva, aortic trunk and aortic branches, whereas the detection subnet is devised to locate five landmarks on the aorta to facilitate morphology measures. The networks share a common encoder and run decoders in parallel, taking full advantage of the synergy of the segmentation and landmark detection tasks. Furthermore, the volume of interest (VOI) module and the squeeze-and-excitation (SE) block with attention mechanisms are incorporated to further boost the capability of feature learning.
Benefiting from the multitask framework, we achieved a mean Dice score of 0.95, average symmetric surface distance of 0.53 mm, Hausdorff distance of 2.13 mm for aortic segmentation, and mean square error (MSE) of 3.23 mm for landmark localization in 40 testing cases.
We proposed a multitask learning framework which can perform segmentation of the thoracic aorta and localization of landmarks simultaneously and achieved good results. It can support quantitative measurement of aortic morphology for further analysis of aortic diseases, such as hypertension.
摘要：
背景：非对比胸部CT广泛用于肺癌筛查，其图像携带胸主动脉的潜在信息。胸主动脉的形态学评估可能在胸主动脉相关疾病的症状前检测和未来不良事件的风险预测中具有潜在价值。然而,由于这些图像中的脉管系统对比度低，主动脉形态的视觉评估具有挑战性,且高度依赖于医师的经验.
目的：本研究的主要目的是提出一种基于深度学习的新型多任务框架，用于在未增强的胸部CT上同时进行主动脉分割和关键标志定位。次要目标是使用该算法来测量胸主动脉形态的定量特征。
方法：所提出的网络由两个子网组成，以进行分割和地标检测，分别。分割子网旨在划定Valsalva的主动脉窦，主动脉干和主动脉分支，而检测子网被设计为定位主动脉上的五个地标，以促进形态学测量。网络共享一个公共编码器，并行运行解码器，充分利用分割和地标检测任务的协同作用。此外，目标体积（VOI）模块和具有注意力机制的挤压和激励（SE）模块被纳入，以进一步提高特征学习的能力。
结果：受益于多任务框架，我们获得了0.95的平均骰子得分，0.53毫米的平均对称表面距离，主动脉分割的Hausdorff距离为2.13mm，在40个测试案例中，地标定位的均方误差（MSE）为3.23mm。
结论：我们提出了一个多任务学习框架，可以同时执行胸主动脉的分割和标志的定位，并取得了良好的效果。它可以支持主动脉形态的定量测量，以进一步分析主动脉疾病，比如高血压。
目的：本研究的主要目的是提出一种基于深度学习的新型多任务框架，用于在未增强的胸部CT上同时进行主动脉分割和关键标志定位。次要目标是使用该算法来测量胸主动脉形态的定量特征。
方法：所提出的网络由两个子网组成，以进行分割和地标检测，分别。分割子网旨在划定Valsalva的主动脉窦，主动脉干和主动脉分支，而检测子网被设计为定位主动脉上的五个地标，以促进形态学测量。网络共享一个公共编码器，并行运行解码器，充分利用分割和地标检测任务的协同作用。此外，目标体积（VOI）模块和具有注意力机制的挤压和激励（SE）模块被纳入，以进一步提高特征学习的能力。
结果：受益于多任务框架，我们获得了0.95的平均骰子得分，0.53毫米的平均对称表面距离，主动脉分割的Hausdorff距离为2.13mm，在40个测试案例中，地标定位的均方误差（MSE）为3.23mm。
结论：我们提出了一个多任务学习框架，可以同时执行胸主动脉的分割和标志的定位，并取得了良好的效果。它可以支持主动脉形态的定量测量，以进一步分析主动脉疾病，比如高血压。
