Weakly Supervised Cell Segmentation by Point Annotation

We propose weakly supervised training schemes to train end-to-end cell segmentation networks that only require a single point annotation per cell as the training label and generate a high-quality segmentation mask close to those fully supervised methods using mask annotation on cells. Three training schemes are investigated to train cell segmentation networks, using the point annotation. First, self-training is performed to learn additional information near the annotated points. Next, co-training is applied to learn more cell regions using multiple networks that supervise each other. Finally, a hybrid-training scheme is proposed to leverage the advantages of both self-training and co-training. During the training process, we propose a divergence loss to avoid the overfitting and a consistency loss to enforce the consensus among multiple co-trained networks. Furthermore, we propose weakly supervised learning with human in the loop, aiming at achieving high segmentation accuracy and annotation efficiency simultaneously. Evaluated on two benchmark datasets, our proposal achieves high-quality cell segmentation results comparable to the fully supervised methods, but with much less amount of human annotation effort.

Automated summary

This study proposes weakly supervised training schemes using point annotations to train cell segmentation networks, including self-training, co-training, and hybrid-training. Divergence loss and consistency loss are introduced during training to prevent overfitting and enhance consensus among networks.