Unsupervised Network Learning for Cell Segmentation

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.

Automated summary

This study introduces a novel unsupervised cell segmentation network learning model, USAR, which trains segmentation networks through adversarial reconstruction without the need for any annotated data.