Four-dimensional label-free live cell image segmentation for predicting live birth potential of mouse embryos
Taichi Kanazawa, Tatsuma Yao, Sora Takeshita, Tatsuki Hirai, Ryo Suenaga, Takahiro Yamada, Yuta Tokuoka, Kazuo Yamagata, Akira Funahashi
The paper develops FL2-Net, a deep learning method for nuclear segmentation in bright-field microscopy images of mouse embryos, achieving higher birth prediction accuracy than existing methods and experts.
bioRxiv · Sep 25, 2024 · PreprintSystems Biology