Abstract
Cell segmentation is a fundamental and critical step in numerous biomedical image studies. For the fully-supervised cell segmentation algorithms, although highly effective, a large quantity of high-quality training data is required, which is usually labor-intensive to produce. In this work, we formulate the unsupervised cell segmentation as a slightly under-constrained problem, and present the Unsupervised Segmentation network learning by Adversarial Reconstruction (USAR), a novel model able to train cell segmentation networks without any annotation. The key idea is to leverage adversarial learning paradigm to train the segmentation network by adversarially reconstructing the input images based on their segmentation results generated by the segmentation network. The USAR model demonstrates its promising application on training segmentation networks in an unsupervised manner, on two benchmark datasets. The implementation of this project can be found at https://github.com/LiangHann/USAR.
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Acknowledgement
This project was supported by Stony Brook University - Brookhaven National Laboratory (SBU-BNL) seed grant on annotation-efficient deep learning.
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Han, L., Yin, Z. (2021). Unsupervised Network Learning for Cell Segmentation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_27
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DOI: https://doi.org/10.1007/978-3-030-87193-2_27
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