Investigating local spatially-enhanced structural and textural descriptors for classification of iPSC colony images

Gizatdinova, Yulia; Rasku, Jyrki; Haponen, Markus; Joutsijoki, Henry; Baldin, Ivan; Paci, Michelangelo; Hyttinen, Jari; Aalto‐Setälä, Katriina; Juhola, Martti

doi:10.1109/embc.2014.6944343

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…In our previous study [ 13 ], the feeder-free system was examined whereas in the following reports [ 14 , 15 ] the more challenging system with feeder has been analyzed. A common factor for our previous studies with feeders [ 14 , 15 ] was that intensity histograms were used as a feature.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

Joutsijoki

Haponen

Rasku

et al. 2016

Computational and Mathematical Methods in Medicine

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The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

Joutsijoki

Haponen

Rasku

et al. 2016

Computational and Mathematical Methods in Medicine

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Classification of iPSC colony images using hierarchical strategies with support vector machines

Joutsijoki

Rasku

Haponen

et al. 2014

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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

et al. 2017

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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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Investigating local spatially-enhanced structural and textural descriptors for classification of iPSC colony images

Cited by 3 publications

References 12 publications

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

Classification of iPSC colony images using hierarchical strategies with support vector machines

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

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