Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells

• Published in: PloS one Vol. 12; no. 12; p. e0189974
• Main Authors: Kavitha, Muthu Subash, Kurita, Takio, Park, Soon-Yong, Chien, Sung-Il, Bae, Jae-Sung, Ahn, Byeong-Cheol
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.12.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Artificial intelligence; Artificial neural networks; Automation; Biology and Life Sciences; Cell culture; Cell differentiation; Classification; Classifiers; Colonies; Computer and Information Sciences; Engineering; Expression vectors; Feature extraction; Fibroblasts; Induced Pluripotent Stem Cells - cytology; Labeling; Medical research; Mice; Microscopy; Model accuracy; Morphology; Neural networks; Neural Networks (Computer); Nuclear medicine; Physical Sciences; Pluripotency; Quality; Quality assessment; Recognition; Research and Analysis Methods; Stem cells; Studies; Support Vector Machine; Support vector machines; Technology application; Therapeutic applications; Transfer functions
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0189974

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.