Kavli Affiliate: Daniel Needleman
| First 5 Authors: Stanislav Lukyanenko, Won-Dong Jang, Donglai Wei, Robbert Struyven, Yoon Kim
| Summary:
The developmental process of embryos follows a monotonic order. An embryo can
progressively cleave from one cell to multiple cells and finally transform to
morula and blastocyst. For time-lapse videos of embryos, most existing
developmental stage classification methods conduct per-frame predictions using
an image frame at each time step. However, classification using only images
suffers from overlapping between cells and imbalance between stages. Temporal
information can be valuable in addressing this problem by capturing movements
between neighboring frames. In this work, we propose a two-stream model for
developmental stage classification. Unlike previous methods, our two-stream
model accepts both temporal and image information. We develop a linear-chain
conditional random field (CRF) on top of neural network features extracted from
the temporal and image streams to make use of both modalities. The linear-chain
CRF formulation enables tractable training of global sequential models over
multiple frames while also making it possible to inject monotonic development
order constraints into the learning process explicitly. We demonstrate our
algorithm on two time-lapse embryo video datasets: i) mouse and ii) human
embryo datasets. Our method achieves 98.1 % and 80.6 % for mouse and human
embryo stage classification, respectively. Our approach will enable more
profound clinical and biological studies and suggests a new direction for
developmental stage classification by utilizing temporal information.
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