A time-lapse embryo dataset for morphokinetic parameter prediction

Abstract

One of the most common treatments for infertile couples is In Vitro Fertilization (IVF). It consists of controlled ovarian hyperstimulation, followed by ovum pickup, fertilization, and embryo culture for 2-6 days under controlled environmental conditions, leading to intrauterine transfer or freezing of embryos identified as having a good implantation potential by embryologists. To allow continuous monitoring of embryo development, Time-lapse imaging incubators (TLI) were first released in the IVF market around 2010. This time-lapse technology provides a dynamic overview of embryonic in vitro development by taking photographs of each embryo at regular intervals throughout its development. TLI appears to be the most promising solution to improve embryo quality assessment methods, and subsequently the clinical efficiency of IVF. In particular, the unprecedented high volume of high-quality images produced by TLI systems has already been leveraged using modern Artificial Intelligence (AI) methods, like deep learning (DL). An important limitation to the development of AI-based solutions for IVF is the absence of a public reference dataset to train and evaluate deep learning (DL) models. In this work, we describe a fully annotated dataset of 704 TLI videos of developing embryos with all 7 focal planes available, for a total of 2,4M images. Of note, we propose highly detailed annotations with 16 different development phases, including early cell division phases, but also late cell divisions, phases after morulation, and very early phases, which have never been used before. This is the first public dataset that will allow the community to evaluate morphokinetic models and the first step towards deep learning-powered IVF. We postulate that this dataset will help improve the overall performance of DL approaches on time-lapse videos of embryo development, ultimately benefiting infertile patients with improved clinical success rates.

Keywords: Computer vision; Deep learning; Human reproduction; IVF; Time-lapse; Videos.

Fig. 1

The method used to assign a label to every frame of the video. First, we identify at which frame each event occurs and assign to these frames a label corresponding to the event they show. The other frames are assigned the label corresponding to the most recent event that has occurred in the previous frames. Note that all frames are labeled except the frames before tPB2 as they precede all the events. The video used as an example here is AG274-2.

Fig. 2

Statistics of the dataset. (a) The number of images per phase in the dataset. (b) Distribution of the number of phases per video in the dataset.

Fig. 3

Illustrations of the 16 development phases used.