Darius Dirvanauskas1, Rytis Maskeliunas1, Vidas Raudonis2, Robertas Damasevicius3. 1. Faculty of Informatics, Multimedia Engineering Department, Kaunas University of Technology, Kaunas, Lithuania. 2. Faculty of Electrical Engineering, Control Systems Department, Kaunas University of Technology, K. Baršausko St. 59-A338, LT-51423 Kaunas, Lithuania. 3. Faculty of Informatics, Multimedia Engineering Department, Kaunas University of Technology, Kaunas, Lithuania. Electronic address: robertas.damasevicius@ktu.lt.
Abstract
BACKGROUND AND OBJECTIVE: Time-lapse microscopy has become an important tool for studying the embryo development process. Embryologists can monitor the entire embryo growth process and thus select the best embryos for fertilization. This time and the resource consuming process are among the key factors for success of pregnancies. Tools for automated evaluation of the embryo quality and development stage prediction are developed for improving embryo selection. METHODS: We present two-classifier vote-based method for embryo image classification. Our classification algorithms have been trained with features extracted using a Convolutional Neural Network (CNN). Prediction of embryo development stage is then completed by comparing confidence of two classifiers. Images are labeled depending on which one receives a larger confidence rating. RESULTS: The evaluation has been done with imagery of real embryos, taken in the ESCO Time Lapse incubator from four different developing embryos. The results illustrate the most effective combination of two classifiers leading to an increase of prediction accuracy and achievement of overall 97.62% accuracy for a test set classification. CONCLUSIONS: We have presented an approach for automated prediction of the embryo development stage for microscopy time-lapse incubator image. Our algorithm has extracted high-complexity image feature using CNN. Classification is done by comparing prediction of two classifiers and selecting the label of that classifier, which has a higher confidence value. This combination of two classifiers has allowed us to increase the overall accuracy of CNN from 96.58% by 1.04% up to 97.62%. The best results are achieved when combining the CNN and Discriminant classifiers. Practical implications include improvement of embryo selection process for in vitro fertilization.
BACKGROUND AND OBJECTIVE: Time-lapse microscopy has become an important tool for studying the embryo development process. Embryologists can monitor the entire embryo growth process and thus select the best embryos for fertilization. This time and the resource consuming process are among the key factors for success of pregnancies. Tools for automated evaluation of the embryo quality and development stage prediction are developed for improving embryo selection. METHODS: We present two-classifier vote-based method for embryo image classification. Our classification algorithms have been trained with features extracted using a Convolutional Neural Network (CNN). Prediction of embryo development stage is then completed by comparing confidence of two classifiers. Images are labeled depending on which one receives a larger confidence rating. RESULTS: The evaluation has been done with imagery of real embryos, taken in the ESCO Time Lapse incubator from four different developing embryos. The results illustrate the most effective combination of two classifiers leading to an increase of prediction accuracy and achievement of overall 97.62% accuracy for a test set classification. CONCLUSIONS: We have presented an approach for automated prediction of the embryo development stage for microscopy time-lapse incubator image. Our algorithm has extracted high-complexity image feature using CNN. Classification is done by comparing prediction of two classifiers and selecting the label of that classifier, which has a higher confidence value. This combination of two classifiers has allowed us to increase the overall accuracy of CNN from 96.58% by 1.04% up to 97.62%. The best results are achieved when combining the CNN and Discriminant classifiers. Practical implications include improvement of embryo selection process for in vitro fertilization.