Pezeshk Aria, Sahiner Berkman, Zeng Rongping, Wunderlich Adam, Chen Weijie, Petrick Nicholas
Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA.
U.S. Food and Drug Administration.
IEEE Trans Biomed Eng. 2015 Dec;62(12):2812-2827. doi: 10.1109/TBME.2015.2445054. Epub 2015 Jun 12.
The availability of large medical image datasets is critical in many applications, such as training and testing of computer-aided diagnosis systems, evaluation of segmentation algorithms, and conducting perceptual studies. However, collection of data and establishment of ground truth for medical images are both costly and difficult. To address this problem, we are developing an image blending tool that allows users to modify or supplement existing datasets by seamlessly inserting a lesion extracted from a source image into a target image. In this study, we focus on the application of this tool to pulmonary nodules in chest CT exams. We minimize the impact of user skill on the perceived quality of the composite image by limiting user involvement to two simple steps: the user first draws a casual boundary around a nodule in the source, and, then, selects the center of desired insertion area in the target. We demonstrate the performance of our system on clinical samples, and report the results of a reader study evaluating the realism of inserted nodules compared to clinical nodules. We further evaluate our image blending techniques using phantoms simulated under different noise levels and reconstruction filters. Specifically, we compute the area under the ROC curve of the Hotelling observer (HO) and noise power spectrum of regions of interest enclosing native and inserted nodules, and compare the detectability, noise texture, and noise magnitude of inserted and native nodules. Our results indicate the viability of our approach for insertion of pulmonary nodules in clinical CT images.
大型医学图像数据集的可用性在许多应用中至关重要，例如计算机辅助诊断系统的训练和测试、分割算法的评估以及进行感知研究。然而，医学图像的数据收集和建立真值既昂贵又困难。为了解决这个问题，我们正在开发一种图像融合工具，该工具允许用户通过将从源图像中提取的病变无缝插入目标图像来修改或补充现有数据集。在本研究中，我们专注于该工具在胸部CT检查中肺结节的应用。我们通过将用户参与限制为两个简单步骤来最小化用户技能对合成图像感知质量的影响：用户首先在源图像中的结节周围绘制一个大致的边界，然后在目标图像中选择所需插入区域的中心。我们展示了我们的系统在临床样本上的性能，并报告了一项读者研究的结果，该研究评估了插入结节与临床结节相比的逼真度。我们还使用在不同噪声水平和重建滤波器下模拟的体模进一步评估了我们的图像融合技术。具体来说，我们计算了霍特林观察者（HO）的ROC曲线下面积以及包含原始结节和插入结节的感兴趣区域的噪声功率谱，并比较了插入结节和原始结节的可检测性、噪声纹理和噪声幅度。我们的结果表明了我们的方法在临床CT图像中插入肺结节的可行性。
