Schwemmer C, Forman C, Wetzl J, Maier A, Hornegger J
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, D-91058 Erlangen, Germany. Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Str. 6, D-91052 Erlangen, Germany.
Phys Med Biol. 2014 Sep 7;59(17):5163-74. doi: 10.1088/0031-9155/59/17/5163. Epub 2014 Aug 20.
We present a software, called CoroEval, for the evaluation of 3D coronary vessel reconstructions from clinical data. It runs on multiple operating systems and is designed to be independent of the imaging modality used. At this point, its purpose is the comparison of reconstruction algorithms or acquisition protocols, not the clinical diagnosis. Implemented metrics are vessel sharpness and diameter. All measurements are taken from the raw intensity data to be independent of display windowing functions. The user can either import a vessel centreline segmentation from other software, or perform a manual segmentation in CoroEval. An automated segmentation correction algorithm is provided to improve non-perfect centrelines. With default settings, measurements are taken at 1 mm intervals along the vessel centreline and from 10 different angles at each measurement point. This allows for outlier detection and noise-robust measurements without the burden and subjectivity a manual measurement process would incur. Graphical measurement results can be directly exported to vector or bitmap graphics for integration into scientific publications. Centreline and lumen segmentations can be exported as point clouds and in various mesh formats. We evaluated the diameter measurement process using three phantom datasets. An average deviation of 0.03 ± 0.03 mm was found. The software is available in binary and source code form at http://www5.cs.fau.de/CoroEval/.
我们展示了一款名为CoroEval的软件,用于根据临床数据评估三维冠状动脉重建。它可在多个操作系统上运行,并且设计为独立于所使用的成像模态。目前,其目的是比较重建算法或采集协议,而非用于临床诊断。实现的度量标准是血管清晰度和直径。所有测量均从原始强度数据获取,以独立于显示窗口函数。用户既可以从其他软件导入血管中心线分割结果,也可以在CoroEval中进行手动分割。提供了一种自动分割校正算法来改善不完美的中心线。在默认设置下,沿着血管中心线以1毫米的间隔进行测量,并且在每个测量点从10个不同角度进行测量。这允许进行异常值检测和抗噪声测量,而无需手动测量过程所带来的负担和主观性。图形化测量结果可以直接导出为矢量图或位图,以便集成到科学出版物中。中心线和管腔分割结果可以作为点云并以各种网格格式导出。我们使用三个体模数据集评估了直径测量过程。发现平均偏差为0.03±0.03毫米。该软件以二进制和源代码形式可在http://www5.cs.fau.de/CoroEval/获取。