An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization

Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potential, clinical embryologists routinely work with static images (morphological assessment) or short video sequences (time-lapse annotation). Recently, Artificial Intelligence models were utilized to support the embryo selection procedure. Even though they have proven their great potential in different in vitro fertilization settings, there is still considerable room for improvement. To support the advancement of algorithms in this research field, we built a dataset consisting of static blastocyst images and additional annotations. As such, Gardner criteria annotations, depicting a morphological blastocyst rating scheme, and collected clinical parameters are provided. The presented dataset is intended to be used to train deep learning models on static morphological images to predict Gardner's criteria and clinical outcomes such as live birth. A benchmark of human expert's performance in annotating Gardner criteria is provided.

Automated summary

Medical Assisted Reproduction has proven effective in treating most forms of infertility. The selection and transfer of embryos with the highest developmental potential is a key procedure in this treatment. Recent use of Artificial Intelligence models in embryo selection has shown great potential, but there is still room for improvement. To support algorithm advancement in this field, a dataset of static blastocyst images and annotations depicting morphological criteria and clinical parameters was built, providing a benchmark for human expert performance in annotating Gardner criteria.