论文标题
基于生成的气道和胸部CT图像上的血管形态定量
Generative-based Airway and Vessel Morphology Quantification on Chest CT Images
论文作者
论文摘要
准确，准确地表征了来自计算机断层扫描（CT）图像（例如气道和血管）的小肺结构的形态，对于诊断肺部疾病的诊断非常重要。较小的导电气道是慢性阻塞性肺部疾病（COPD）中气流耐药性增加的主要部位，而准确的尺寸容器可以帮助鉴定肺部区域的动脉和静脉变化，从而确定未来疾病。但是，由于图像分辨率和工件，传统方法通常受到限制。 我们提出了一个卷积神经回归器（CNR），该神经回归器（CNR）可提供气道管腔，气道壁厚和容器半径的横截面测量。 CNR经过由合成结构的生成模型创建的数据训练，该模型与模拟且无监督的生成对抗网络（SIMGAN）结合使用，以创建具有已知地面真相的模拟和精制的气道和容器。 为了进行验证，我们首先使用拟议的生成模型生成的合成生成的气道和容器来计算相对误差，并与传统方法相比直接评估CNR的准确性。然后，通过分析一秒钟（FEV1 \％）的预测强迫呼气量的百分比与PI10参数的值（肺功能和气道疾病的两种众所周知的测量）来进行体内验证。对于血管，我们评估了小血管血容量的估计与肺部一氧化碳（DLCO）的扩散能力之间的相关性。 结果表明，卷积神经网络（CNN）为与生理相关性的胸部CT图像上的胸部CT图像进行准确测量血管和气道提供了一个有希望的方向。
Accurately and precisely characterizing the morphology of small pulmonary structures from Computed Tomography (CT) images, such as airways and vessels, is becoming of great importance for diagnosis of pulmonary diseases. The smaller conducting airways are the major site of increased airflow resistance in chronic obstructive pulmonary disease (COPD), while accurately sizing vessels can help identify arterial and venous changes in lung regions that may determine future disorders. However, traditional methods are often limited due to image resolution and artifacts. We propose a Convolutional Neural Regressor (CNR) that provides cross-sectional measurement of airway lumen, airway wall thickness, and vessel radius. CNR is trained with data created by a generative model of synthetic structures which is used in combination with Simulated and Unsupervised Generative Adversarial Network (SimGAN) to create simulated and refined airways and vessels with known ground-truth. For validation, we first use synthetically generated airways and vessels produced by the proposed generative model to compute the relative error and directly evaluate the accuracy of CNR in comparison with traditional methods. Then, in-vivo validation is performed by analyzing the association between the percentage of the predicted forced expiratory volume in one second (FEV1\%) and the value of the Pi10 parameter, two well-known measures of lung function and airway disease, for airways. For vessels, we assess the correlation between our estimate of the small-vessel blood volume and the lungs' diffusing capacity for carbon monoxide (DLCO). The results demonstrate that Convolutional Neural Networks (CNNs) provide a promising direction for accurately measuring vessels and airways on chest CT images with physiological correlates.
