Danilov Viacheslav V, Skirnevskiy Igor P, Gerget Olga M, Shelomentcev Egor E, Kolpashchikov Dmitrii Yu, Vasilyev Nikolay V
Medical Devices Design Laboratory, RASA Center in Tomsk, Tomsk Polytechnic University, Tomsk, Russia.
Cardiac Surgery Department, Boston Children's Hospital, Boston, USA.
Int J Cardiovasc Imaging. 2018 Jul;34(7):1041-1055. doi: 10.1007/s10554-018-1314-4. Epub 2018 Feb 10.
The present study aimed to present a workflow algorithm for automatic processing of 2D echocardiography images. The workflow was based on several sequential steps. For each step, we compared different approaches. Epicardial 2D echocardiography datasets were acquired during various open-chest beating-heart surgical procedures in three porcine hearts. We proposed a metric called the global index that is a weighted average of several accuracy coefficients, indices and the mean processing time. This metric allows the estimation of the speed and accuracy for processing each image. The global index ranges from 0 to 1, which facilitates comparison between different approaches. The second step involved comparison among filtering, sharpening and segmentation techniques. During the noise reduction step, we compared the median filter, total variation filter, bilateral filter, curvature flow filter, non-local means filter and mean shift filter. To clarify the endocardium borders of the right heart, we used the linear sharpen. Lastly, we applied watershed segmentation, clusterisation, region-growing, morphological segmentation, image foresting segmentation and isoline delineation. We assessed all the techniques and identified the most appropriate workflow for echocardiography image segmentation of the right heart. For successful processing and segmentation of echocardiography images with minimal error, we found that the workflow should include the total variation filter/bilateral filter, linear sharpen technique, isoline delineation/region-growing segmentation and morphological post-processing. We presented an efficient and accurate workflow for the precise diagnosis of cardiovascular diseases. We introduced the global index metric for image pre-processing and segmentation estimation.
本研究旨在提出一种用于二维超声心动图图像自动处理的工作流程算法。该工作流程基于几个连续的步骤。对于每个步骤，我们比较了不同的方法。在三只猪心脏的各种开胸心脏跳动手术过程中获取了心外膜二维超声心动图数据集。我们提出了一种称为全局指数的度量标准，它是几个准确度系数、指数和平均处理时间的加权平均值。该度量标准允许估计处理每个图像的速度和准确性。全局指数范围从0到1，这便于不同方法之间的比较。第二步涉及滤波、锐化和分割技术之间的比较。在降噪步骤中，我们比较了中值滤波器、全变差滤波器、双边滤波器、曲率流滤波器、非局部均值滤波器和均值漂移滤波器。为了清晰显示右心的心内膜边界，我们使用了线性锐化。最后，我们应用了分水岭分割、聚类、区域生长、形态学分割、图像森林分割和等值线描绘。我们评估了所有技术，并确定了右心超声心动图图像分割最合适的工作流程。为了以最小的误差成功处理和分割超声心动图图像，我们发现工作流程应包括全变差滤波器/双边滤波器、线性锐化技术、等值线描绘/区域生长分割和形态学后处理。我们提出了一种用于心血管疾病精确诊断的高效且准确的工作流程。我们引入了用于图像预处理和分割估计的全局指数度量标准。
