Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
Department of Electronic Engineering, Tsinghua University, Beijing, China.
Comput Med Imaging Graph. 2018 Jun;66:1-13. doi: 10.1016/j.compmedimag.2018.02.003. Epub 2018 Feb 17.
Lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images are critical for assessing the dimensions of vascular structures and providing plaque information in the diagnosis and navigation of vascular interventions. However, manual delineation of the lumen and MA borders is an intricate and time-consuming process. In this paper, a texture-enhanced deformable model (TEDM) is proposed to accurately detect these borders by incorporating texture information with the morphological factors of deformable model. An ensemble support vector machine classifier is used to classify IVUS pixels presented by texture features into different tissue types. The image regionalization maps of different tissue types are further used for texture enhancement modules in the TEDM. The proposed TEDM method has been tested on 1500 images from 15 clinical IVUS datasets by comparing with the manual delineations. Evaluation results demonstrate that our method can accurately detect lumen and MA surfaces with small surface distance errors of 0.17 and 0.19 mm, respectively. Accurate segmentation results provide 2D measurements of MA/lumen areas and 3D vessel visualizations for vascular interventions.
血管内超声（IVUS）图像中的管腔和中膜-外膜（MA）边界对于评估血管结构的尺寸和提供斑块信息在血管介入的诊断和导航中至关重要。然而，手动描绘管腔和 MA 边界是一个复杂且耗时的过程。在本文中，我们提出了一种纹理增强的可变形模型（TEDM），通过将纹理信息与可变形模型的形态学因素相结合，准确地检测这些边界。我们使用集成支持向量机分类器将由纹理特征表示的 IVUS 像素分类为不同的组织类型。不同组织类型的图像分区图进一步用于 TEDM 中的纹理增强模块。我们的方法已经在 15 个临床 IVUS 数据集的 1500 张图像上进行了测试，并与手动描绘进行了比较。评估结果表明，我们的方法可以准确地检测管腔和 MA 表面，表面距离误差分别为 0.17 和 0.19mm。准确的分割结果为血管介入提供了 MA/管腔面积的二维测量和三维血管可视化。
