Abstract
In Vitro Fertilization (IVF) treatment is increasingly chosen by couples suffering from infertility as a means to conceive. Time-lapse imaging technology has enabled continuous monitoring of embryos in vitro and time-based development metrics for assessing embryo quality prior to transfer. Timing at which embryos reach certain development stages provides valuable information about their potential to become a positive pregnancy. Automating development stage detection of day 4–5 embryos remains difficult due to small variation between stages. In this paper, a classifier is trained to detect embryo development stage with learning strategies added to explicitly address challenges of this task. Synergic loss encourages the network to recognize and utilize stage similarities between different embryos. Short-range temporal learning incorporates chronological order to embryo sequence predictions. Image and sequence augmentations complement both approaches to increase generalization to unseen sequences. The proposed approach was applied to human embryo sequences with labeled morula and blastocyst stage onsets. Morula and blastocyst stage classification was improved by 5.71% and 1.11%, respectively, while morula and blastocyst stage mean absolute onset error was reduced by 19.1% and 8.7%, respectively. Code is available: https://github.com/llockhar/Embryo-Stage-Onset-Detection.
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Lockhart, L., Saeedi, P., Au, J., Havelock, J. (2021). Automating Embryo Development Stage Detection in Time-Lapse Imaging with Synergic Loss and Temporal Learning. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_52
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