Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning

Embryo quality assessment after in vitro fertilization (IVF) is primarily done visually by embryologists. Variability among assessors, however, remains one of the main causes of the low success rate of IVF. This study aims to develop an automated embryo assessment based on a deep learning model. This study includes a total of 1084 images from 1226 embryos. The images were captured by an inverted microscope at day 3 after fertilization. The images were labelled based on Veeck criteria that differentiate embryos to grade 1 to 5 based on the size of the blastomere and the grade of fragmentation. Our deep learning grading results were compared to the grading results from trained embryologists to evaluate the model performance. Our best model from fine-tuning a pre-trained ResNet50 on the dataset results in 91.79% accuracy. The model presented could be developed into an automated embryo assessment method in point-of-care settings.

[1]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[2]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  P. Eftekhari-Yazdi,et al.  An Overview of The Available Methods for Morphological Scoring of Pre-Implantation Embryos in In Vitro Fertilization , 2015, Cell journal.

[4]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[5]  Nikica Zaninovic,et al.  Artificial intelligence: its applications in reproductive medicine and the assisted reproductive technologies. , 2019, Fertility and sterility.

[6]  A. Sparks.  An Atlas of Human Gametes and Conceptuses: An Illustrated Reference for Assisted Reproductive Technology , 2000 .

[7]  S. Coskun,et al.  Day 5 versus day 3 embryo transfer: a controlled randomized trial. , 2000, Human reproduction.

[8]  Jacob F Mayer,et al.  Interobserver and intraobserver variation in day 3 embryo grading. , 2006, Fertility and sterility.

[9]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Ehsan Kazemi,et al.  Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization , 2019, npj Digital Medicine.

[12]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[13]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[14]  Henrik Karstoft,et al.  Automatic grading of human blastocysts from time-lapse imaging , 2019, Comput. Biol. Medicine.

[15]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Ian Huang,et al.  Using Deep Learning with Large Dataset of Microscope Images to Develop an Automated Embryo Grading System , 2019, Fertility & Reproduction.

[17]  Jeremy Howard,et al.  fastai: A Layered API for Deep Learning , 2020, Inf..

[18]  Ş. Hatırnaz,et al.  Day 3 embryo transfer versus day 5 blastocyst transfers: A prospective randomized controlled trial , 2017, Turkish journal of obstetrics and gynecology.

[19]  F. Kung,et al.  The predictive value of using a combined Z-score and day 3 embryo morphology score in the assessment of embryo survival on day 5. , 2003, Human reproduction.

[20]  J. Shaw,et al.  A formula for scoring human embryo growth rates in in vitro fertilization: Its value in predicting pregnancy and in comparison with visual estimates of embryo quality , 1986, Journal of in Vitro Fertilization and Embryo Transfer.

[21]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Leslie N. Smith,et al.  Cyclical Learning Rates for Training Neural Networks , 2015, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[23]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..