Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB.
Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB.
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DOI：
10.1186/s12860-021-00369-3
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发表时间：
2021-06-02
影响因子：
2.8
通讯作者：
Stiehm M
中科院分区：
文献类型：
--
作者：
Oldenburg J;Maletzki L;Strohbach A;Bellé P;Siewert S;Busch R;Felix SB;Schmitz KP;Stiehm M
在临床结果的背景下，部署心血管装置后的内皮愈合尤为重要。因此，开发工具来精确预测种植体部署过程中损伤后内皮细胞的生长是非常有意义的。对于再内皮化的实验研究，通常采用体外细胞迁移法。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有希望的机会。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。在体外伤口愈合实验中，我们比较了传统的分析方法和我们的iCD方法。因此，我们在细胞尺度上确定了细胞密度和细胞速度，在群体尺度上确定了细胞层的运动以及两个细胞单层之间的间隙闭合。我们的数据表明，基于深度学习算法的细胞密度分析在对图像失真的鲁棒性方面优于自适应阈值方法。此外，iCD在细胞尺度上获得的结果与人工速度检测一致，而传统方法，如细胞图像测速（CIV），低估了0.5倍的细胞速度。此外，我们发现单层运动的iCD分析给出的结果与手工徒手检测一样好，而传统方法再次显示出与手工检测相比更多的磨损前缘检测。ICD对单层边缘突出的分析结果也与人工估计接近，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，像我们的iCD这样的深度学习算法在伤口愈合分析领域有能力胜过传统方法。使用iCD对细胞和群体规模进行联合分析非常适合于节省时间和高质量的伤口愈合分析，使研究团体能够详细了解内皮运动。
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.
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影响因子：
--
作者：
Fazeli E;Roy NH;Follain G;Laine RF;von Chamier L;Hänninen PE;Eriksson JE;Tinevez JY;Jacquemet G
通讯作者：
Jacquemet G
影响因子：
--
作者：
Buetti-Dinh, Antoine;Galli, Vanni;Dopson, Mark
通讯作者：
Dopson, Mark
影响因子：
2.5
作者：
Milde, Florian;Franco, Davide;Koumoutsakos, Petros
通讯作者：
Koumoutsakos, Petros
DOI：
10.1016/j.jormas.2019.06.002
发表时间：
2019-09-01
影响因子：
2.2
作者：
Fourcade, A.;Khonsari, R. H.
通讯作者：
Khonsari, R. H.
影响因子：
2
作者：
Jin, Wang;Shah, Esha T.;Simpson, Matthew J.
通讯作者：
Simpson, Matthew J.