Purpose

Drug induced cardiac toxicity is a disruption of the functionality of cardiomyocytes which is highly correlated to the organization of the subcellular structures. We can analyze cellular structures by utilizing microscopy imaging data. However, conventional image analysis methods might miss structural deteriorations that are difficult to perceive. Here, we propose an image-based deep learning pipeline for the automated quantification of drug induced structural deteriorations using a 3D heart slice culture model.

Methods

In our deep learning pipeline, we quantify the induced structural deterioration from three anticancer drugs (doxorubicin, sunitinib, and herceptin) with known adverse cardiac effects. The proposed deep learning framework is composed of three convolutional neural networks that process three different image sizes. The results of the three networks are combined to produce a classification map that shows the locations of the structural deteriorations in the input cardiac image.

Results

The result of our technique is the capability of producing classification maps that accurately detect drug induced structural deterioration on the pixel level.

Conclusion

This technology could be widely applied to perform unbiased quantification of the structural effect of the cardiotoxins on heart slices.

中文翻译：

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基于人工智能的框架来量化心脏切片中心肌细胞的结构完整性

目的

药物诱导的心脏毒性是心肌细胞功能的破坏，这与亚细胞结构的组织高度相关。我们可以利用显微镜成像数据分析细胞结构。然而，传统的图像分析方法可能会遗漏难以察觉的结构恶化。在这里，我们提出了一种基于图像的深度学习管道，用于使用 3D 心脏切片培养模型自动量化药物引起的结构恶化。

方法

在我们的深度学习管道中，我们量化了三种具有已知心脏不良反应的抗癌药物（阿霉素、舒尼替尼和赫赛汀）引起的结构恶化。所提出的深度学习框架由处理三种不同图像大小的三个卷积神经网络组成。三个网络的结果结合起来产生一个分类图，显示输入心脏图像中结构恶化的位置。

结果

我们技术的结果是生成分类图的能力，可以在像素级别上准确检测药物引起的结构恶化。

结论

该技术可广泛应用于对心脏毒素对心脏切片的结构影响进行无偏量化。