Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions.
Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions.
复制标题
DOI：
10.1007/s11548-017-1657-7
复制
发表时间：
2017-11
影响因子：
3
通讯作者：
van Walsum T
中科院分区：
文献类型：
--
作者：
Zahnd G;Hoogendoorn A;Combaret N;Karanasos A;Péry E;Sarry L;Motreff P;Niessen W;Regar E;van Soest G;Gijsen F;van Walsum T
在临床竞技场中，冠状动脉内光学相干断层扫描图像的定量和自动分析对于评估心血管风险是有用且节省时间的。首先，内膜，中膜和外膜层的界面被分割，通过原始的前传播方案，在4D多参数空间中运行，以同时提取初始横截面图像中的三个非交叉轮廓。第二，通过机器学习方法利用由暂定轮廓产生的信息来识别动脉壁的健康和患病区域。框架是完全自动的。该方法适用于40例来自两个不同的医疗中心。该框架在140张图像上进行了训练，并在260张其他图像上进行了验证。对于轮廓分割方法，内膜-中膜界面、中膜-外膜界面和外膜-外膜周界面的平均分割误差。分类方法显示出良好的准确性，中值Dice系数等于0.93，四分位数范围为（0.78-0.98）。所提出的框架表现出有前途的离线性能，并可能被转化为各种临床应用的可靠工具，如量化的组织层厚度和整体总结的健康区域在整个回调。
Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were for the intima–media interface, for the media–adventitia interface, and for the adventitia–periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78–0.98). The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.
登录
查看更多内容
影响因子：
11.3
作者：
Bezerra, Hiram G.;Costa, Marco A.;Guagliumi, Giulio;Rollins, Andrew M.;Simon, Daniel I.
通讯作者：
Simon, Daniel I.
DOI：
10.1016/j.compmedimag.2013.09.004
发表时间：
2014-03-01
影响因子：
5.7
作者：
Destrempes, Francois;Cardinal, Marie-Helene Roy;Cloutier, Guy
通讯作者：
Cloutier, Guy
影响因子：
10.6
作者：
Kauffmann, Claude;Motreff, Pascal;Sarry, Laurent
通讯作者：
Sarry, Laurent
影响因子：
2.1
作者：
Ughi, G. J.;Adriaenssens, T.;D'hooge, J.
通讯作者：
D'hooge, J.
影响因子：
5.8
作者：
Kursa, Miron B.;Rudnicki, Witold R.
通讯作者：
Rudnicki, Witold R.