Institute of Anatomy and Cell Biology, Martin Luther University Halle-Wittenberg, Germany.
Department of Radiotherapy, Martin Luther University Halle-Wittenberg, Germany.
PLoS One. 2020 Feb 26;15(2):e0229620. doi: 10.1371/journal.pone.0229620. eCollection 2020.
Ionizing radiation induces amongst other the most critical type of DNA damage: double-strand breaks (DSBs). Efficient repair of such damage is crucial for cell survival and genomic stability. The analysis of DSB associated foci assays is often performed manually or with automatic systems. Manual evaluation is time consuming and subjective, while most automatic approaches are prone to changes in experimental conditions or to image artefacts. Here, we examined multiple machine learning models, namely a multi-layer perceptron classifier (MLP), linear support vector machine classifier (SVM), complement naive bayes classifier (cNB) and random forest classifier (RF), to correctly classify γH2AX foci in manually labeled images containing multiple types of artefacts. All models yielded reasonable agreements to the manual rating on the training images (Matthews correlation coefficient >0.4). Afterwards, the best performing models were applied on images obtained under different experimental conditions. Thereby, the MLP model produced the best results with an F1 Score >0.9. As a consequence, we have demonstrated that the used approach is sufficient to mimic manual counting and is robust against image artefacts and changes in experimental conditions.
电离辐射会导致最严重的一类 DNA 损伤：双链断裂（DSB）。这种损伤的有效修复对于细胞存活和基因组稳定性至关重要。DSB 相关焦点分析通常通过手动或自动系统进行。手动评估既耗时又主观，而大多数自动方法容易受到实验条件变化或图像伪影的影响。在这里，我们检查了多种机器学习模型，即多层感知器分类器（MLP）、线性支持向量机分类器（SVM）、补充朴素贝叶斯分类器（cNB）和随机森林分类器（RF），以正确分类手动标记的包含多种类型伪影的图像中的 γH2AX 焦点。所有模型在训练图像上与手动评分的相关性都很好（马修斯相关系数>0.4）。之后，将表现最好的模型应用于在不同实验条件下获得的图像。结果，MLP 模型产生了最佳的 F1 分数>0.9。因此，我们已经证明，所使用的方法足以模拟手动计数，并且对图像伪影和实验条件变化具有鲁棒性。
