Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.

中文翻译：

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在 MATLAB 中使用深度学习对伤口愈合实验进行全面细胞水平分析的方法

就临床结果而言，部署心血管装置后的内皮愈合尤为重要。因此，开发用于精确预测植入物部署过程中损伤后内皮生长的工具非常有意义。对于体外再内皮化的实验研究，通常使用细胞迁移测定。然而，活细胞图像的半自动分析通常基于灰度值分布，因此受到图像质量和用户依赖性的限制。深度学习算法的兴起为医学图像分析的应用提供了广阔的前景。在这里，我们提出了一种智能细胞检测（iCD）方法，用于综合分析，以获得细胞和群体规模的基本特征。在体外伤口愈合测定中，我们将传统分析方法与我们的 iCD 方法进行了比较。因此，我们确定了细胞规模上的细胞密度和细胞速度，以及细胞层的运动以及群体规模上两个细胞单层之间的间隙闭合。我们的数据表明，在针对图像失真的鲁棒性方面，基于深度学习算法的细胞密度分析优于自适应阈值方法。此外，使用 iCD 获得的细胞规模结果与手动速度检测一致，而细胞图像测速 (CIV) 等传统方法会低估细胞速度 0.5 倍。此外，我们发现单层运动的 iCD 分析给出的结果与手动徒手检测一样，而与手动检测相比，传统方法再次显示出更多磨损的前缘检测。ICD对单层边缘突出的分析也得出了结果，该结果接近于手动估计，相对误差为11.7%。相比之下，传统的 Canny 方法的相对误差为 76.4%。我们的实验结果表明，我们的 iCD 等深度学习算法在伤口愈合分析领域能够超越传统方法。使用 iCD 对细胞和群体规模进行综合分析非常适合省时且高质量的伤口愈合分析，使研究界能够详细了解内皮运动。