Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features

Masuda, Atsuki; Raytchev, Bisser; Kurita, Takio; Imamura, Toru; Suzuki, Masashi; Tamaki, Toru; Kaneda, Kazufumi

doi:10.1007/978-3-319-24888-2_19

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…Classification of hiPSC colony images has recently been studied in [5, 16–18]. The difference between the given references and our paper is significant.…”

Section: Introductionmentioning

confidence: 83%

“…The next step is to return back to the image processing and feature extraction stages and investigate other approaches for this application. Based on [18] and the results it seems that the feature detectors used in computer vision such as SIFT or other closely related techniques are a correct approach for hiPSC colony image classification.…”

Section: Discussionmentioning

confidence: 99%

“…The difference between the given references and our paper is significant. Firstly, Masuda et al [18] had a binary classification problem between good and bad iPSC colonies whereas our problem is a multiclass problem between good and semigood and bad colony classes. Secondly, we use different Scaled Invariant Feature Transformation (SIFT) [19] based features in classification compared to [18] and [16, 17] in which only intensity histograms were used as features.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, Masuda et al [18] had a binary classification problem between good and bad iPSC colonies whereas our problem is a multiclass problem between good and semigood and bad colony classes. Secondly, we use different Scaled Invariant Feature Transformation (SIFT) [19] based features in classification compared to [18] and [16, 17] in which only intensity histograms were used as features. Thirdly, although in [5] we used the same SIFT-based features and image dataset as in this study, in none of the earlier studies has ECOC framework been studied before and, hence, our research fulfills the missing gap.…”

Section: Introductionmentioning

confidence: 99%

“…Thirdly, although in [5] we used the same SIFT-based features and image dataset as in this study, in none of the earlier studies has ECOC framework been studied before and, hence, our research fulfills the missing gap. Fourthly, we have a significantly larger image dataset compared to [16–18]. …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

Joutsijoki

Haponen

Rasku

et al. 2016

BioMed Research International

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The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of them k-Nearest Neighbor (k-NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design and k-NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem.

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“…Classification of hiPSC colony images has recently been studied in [5, 16–18]. The difference between the given references and our paper is significant.…”

Section: Introductionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

Joutsijoki

Haponen

Rasku

et al. 2016

BioMed Research International

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A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction

Fan

Zhang

et al. 2017

Sci Rep

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During cellular reprogramming, the mesenchymal-to-epithelial transition is accompanied by changes in morphology, which occur prior to iPSC colony formation. The current approach for detecting morphological changes associated with reprogramming purely relies on human experiences, which involve intensive amounts of upfront training, human error with limited quality control and batch-to-batch variations. Here, we report a time-lapse-based bright-field imaging analysis system that allows us to implement a label-free, non-invasive approach to measure morphological dynamics. To automatically analyse and determine iPSC colony formation, a machine learning-based classification, segmentation, and statistical modelling system was developed to guide colony selection. The system can detect and monitor the earliest cellular texture changes after the induction of reprogramming in human somatic cells on day 7 from the 20–24 day process. Moreover, after determining the reprogramming process and iPSC colony formation quantitatively, a mathematical model was developed to statistically predict the best iPSC selection phase independent of any other resources. All the computational detection and prediction experiments were evaluated using a validation dataset, and biological verification was performed. These algorithm-detected colonies show no significant differences (Pearson Coefficient) in terms of their biological features compared to the manually processed colonies using standard molecular approaches.

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

et al. 2017

Self Cite

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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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Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features

Cited by 3 publications

References 5 publications

Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images

A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction

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

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