Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.
Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.
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DOI：
10.1371/journal.pone.0189974
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Ahn BC
中科院分区：
文献类型：
--
作者：
Kavitha MS;Kurita T;Park SY;Chien SI;Bae JS;Ahn BC
多能干细胞可以作为研究疾病进展的模型潜在地用于临床应用。这种对细胞中致病事件的跟踪需要不断评估干细胞的质量。现有的方法不足以用于干细胞集落的稳健和自动化分化。在这项研究中，我们开发了一种新的基于向量的卷积神经网络（V-CNN）模型，用于提取诱导多能干细胞（iPSC）集落的特征，以区分集落特征。在CNN的前面生成从特征向量到虚拟图像的传递函数，以便对健康和不健康菌落的特征向量进行分类。提出的V-CNN模型在区分菌落的鲁棒性进行了比较，基于形态，纹理和组合特征的竞争性支持向量机（SVM）分类器。此外，使用五重交叉验证来研究V-CNN模型的性能。V-CNN模型的精确度，召回率和F-测量值相对高于SVM分类器，范围为87- 93%，表明假阳性和假阴性率更少。此外，对于确定菌落的质量，V-CNN模型基于形态（95.5%），纹理（91.0%）和组合（93.2%）特征显示出比SVM分类器（分别为86.7，83.3和83.4%）估计的更高的准确度值。类似地，对于V-CNN模型，使用五重交叉验证的特征集的准确度高于90%，而SVM模型产生的准确度在75- 77%的范围内。因此，我们得出结论，所提出的V-CNN模型优于传统的SVM分类器，这强烈表明它是iPSC稳健集落分类的可靠框架。它还可以作为培养和其他实验过程中具有成本效益的质量识别工具。
Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector–based convolutional neural network (V-CNN) with respect to extracted features of the induced pluripotent stem cell (iPSC) colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies was compared with that of the competitive support vector machine (SVM) classifier based on morphological, textural, and combined features. Additionally, five-fold cross-validation was used to investigate the performance of the V-CNN model. The precision, recall, and F-measure values of the V-CNN model were comparatively higher than those of the SVM classifier, with a range of 87–93%, indicating fewer false positives and false negative rates. Furthermore, for determining the quality of colonies, the V-CNN model showed higher accuracy values based on morphological (95.5%), textural (91.0%), and combined (93.2%) features than those estimated with the SVM classifier (86.7, 83.3, and 83.4%, respectively). Similarly, the accuracy of the feature sets using five-fold cross-validation was above 90% for the V-CNN model, whereas that yielded by the SVM model was in the range of 75–77%. We thus concluded that the proposed V-CNN model outperforms the conventional SVM classifier, which strongly suggests that it as a reliable framework for robust colony classification of iPSCs. It can also serve as a cost-effective quality recognition tool during culture and other experimental procedures.
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DOI：
10.1016/j.ymeth.2015.11.012
发表时间：
2016-03-01
期刊：
Methods (San Diego, Calif.)
影响因子：
--
作者：
Leha A;Moens N;Meleckyte R;Culley OJ;Gervasio MK;Kerz M;Reimer A;Cain SA;Streeter I;Folarin A;Stegle O;Kielty CM;HipSci Consortium;Durbin R;Watt FM;Danovi D
通讯作者：
Danovi D
影响因子：
64.5
作者：
Park IH;Arora N;Huo H;Maherali N;Ahfeldt T;Shimamura A;Lensch MW;Cowan C;Hochedlinger K;Daley GQ
通讯作者：
Daley GQ
影响因子：
4.3
作者：
Nagasaka, Risako;Matsumoto, Megumi;Kato, Ryuji
通讯作者：
Kato, Ryuji
影响因子：
2.7
作者：
Kavitha MS;Asano A;Taguchi A;Kurita T;Sanada M
通讯作者：
Sanada M
影响因子：
4.8
作者：
Burges, CJC
通讯作者：
Burges, CJC