Abstract
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.
Highlights
Induced pluripotent stem cells, which are created from an adult cell that has been reprogrammed, enable the development of an unlimited source of any type of human cells needed for drug discovery and clinical applications [1]. induced pluripotent stem cell (iPSC) are able to help track the earliest disease-causing events in cells and can be used as sources of various cell-based therapies
The potential of each individual morphological and textural feature in distinguishing the iPSC colonies was examined from the area under the curve (AUC), using receiver operating characteristic curve analysis (NCSS 11 Statistical Software, Kaysville, UT, USA)
This study has proposed a new automatic system that interfaces image analysis methods with the vector–based convolutional neural network (V-convolutional neural network (CNN)) model for the segmentation and classification of phase contrast microscopy images, using the morphological and textural features of iPSC colonies
Summary
Induced pluripotent stem cells (iPSCs), which are created from an adult cell that has been reprogrammed, enable the development of an unlimited source of any type of human cells needed for drug discovery and clinical applications [1]. iPSCs are able to help track the earliest disease-causing events in cells and can be used as sources of various cell-based therapies. Because a healthy quality of undifferentiated iPSCs is an essential requisite for further experimental and therapeutic approaches, the rapid and robust estimation of iPSC quality is very important to meet growing demands [2,3,4]. The morphological structure of a healthy or goodquality iPSC colony commonly has tightly compacted round cells and an explicit boundary, whereas unhealthy or bad-quality colonies show a different morphology [5]. The present approach of evaluating the quality of iPSCs on the basis of colony morphology is predominantly subjective and can strongly differ according to individual skills. A quantitative system for the rapid and accurate segmentation and estimation of colony quality is essential in order to reduce classification errors. Removal of the use of fluorescent labeling or other chemical reagents would be helpful in preparing the iPSCs for additional research experiments
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