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

Joutsijoki, Henry; Rasku, Jyrki; Haponen, Markus; Baldin, Ivan; Gizatdinova, Yulia; Paci, Michelangelo; Saarikoski, Jyri; Varpa, Kirsi; Siirtola, Harri; Avalos-Salguero, Jorge; Iltanen, Kati; Laurikkala, Jorma; Penttinen, Kirsi; Hyttinen, Jari; Aalto‐Setälä, Katriina; Juhola, Martti

doi:10.1109/cidm.2014.7008152

Cited by 6 publications

(30 citation statements)

References 13 publications

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“…Only with diagLinear and classification tree above 50% accuracy was obtained. The best choice according to Table 5 was classification tree with OVO coding design having 57.2% accuracy and outperforming results in [16, 17]. Also, with the ternary complete coding design classification tree obtained above 50% accuracy.…”

Section: Resultsmentioning

confidence: 93%

“…iPSC lines were established using the same approach as given in [1] and cell lines were characterized for their karyotypes and pluripotency as described in [33]. Categorization of the iPSC colony images was performed as follows [5, 16, 17]:Good colonies have rounded shape, translucent even color, and defined edges.Semigood colonies have clear edges but include changes in color and structure.Bad colonies have partially lost edge structure, vacuole could sometimes be seen, and areas of three-dimensional structures were observed. …”

Section: Methodsmentioning

confidence: 99%

“…Image data acquisition followed the guidelines given in [5, 16, 17]. We used hiPSCs and colonies were photographed between 5 and 7 d of their weekly growth cycle since within this time frame better visualization of the colonies can be obtained.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, every image is represented by a mean SIFT descriptor and it can be used in classification. This “mean SIFT descriptor” approach improved classification results in [5] compared to [16, 17] and this inspired us to use the same approach again in this paper. When mean SIFT descriptors were obtained, the dataset to be used in classification was size (excluding column of class labels) of 173 × 128 (rows × columns).…”

Section: Methodsmentioning

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%

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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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Section: Resultsmentioning

confidence: 93%