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.
