Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.
Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Comisión de Investigaciones Científicas de la Provincia deBuenos Aires (CICPBA), Argentina.
Comput Methods Programs Biomed. 2019 Aug;177:113-121. doi: 10.1016/j.cmpb.2019.05.021. Epub 2019 May 21.
Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall.
Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces.
The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ± 0.08 for LI segmentation, compared with 0.88 ± 0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ± 0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ± 0.30.
A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements.
血管内超声（IVUS）提供血管的轴向灰度图像。大量的图像需要自动分析，特别是要识别管腔和外血管壁。然而，高噪声、伪影以及分叉、钙化和纤维斑块等解剖结构的存在，通常会阻碍血管壁的正确自动分割。
使用支持向量机（SVM）自动检测管腔、中膜、外膜和周围组织。SVM 的分类性能因图像每个区域内存在的结构类型而异。随机森林（RF）用于检测不同的形态结构，并根据检测到的结构修改初始层分类。所得分类图被馈送到基于可变形轮廓的分割方法中，以检测管腔-内膜（LI）和中膜-外膜（MA）界面。
根据结构的存在对层分类进行修改，证明对 LI 和 MA 分割有效。与半自动方法的 0.88±0.05 相比，所提出的方法达到了 0.88±0.08 的 Jaccard 度量（JM）用于 LI 分割。在 MA 方面，我们的方法达到了 0.84±0.09 的 JM，并且在 HD 方面优于以前的自动方法，为 0.51mm±0.30。
对动脉层分类进行简单修改，可产生与最先进的完全自动分割方法相匹配并改善 20MHz IVUS 图像中 LI 和 MA 的结果。对于 LI 分割，所提出的自动方法与半自动方法一样准确。对于 MA 分割，我们的方法与文献中描述的最先进的自动方法质量相匹配。此外，我们的实现是模块化和开源的，允许进行未来的扩展和改进。
