IEEE Trans Med Imaging. 2021 Oct;40(10):2736-2747. doi: 10.1109/TMI.2020.3046292. Epub 2021 Sep 30.
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.
我们提出了一种弱监督训练方案，用于训练端到端的细胞分割网络，该网络只需要每个细胞的单个点注释作为训练标签，并使用细胞的掩模注释生成与完全监督方法接近的高质量分割掩模。我们研究了三种使用点注释训练细胞分割网络的训练方案。首先，执行自训练以学习注释点附近的附加信息。接下来，应用协同训练使用相互监督的多个网络来学习更多的细胞区域。最后，提出了一种混合训练方案来利用自训练和协同训练的优势。在训练过程中，我们提出了一种散度损失来避免过拟合，以及一种一致性损失来强制多个协同训练网络之间的共识。此外，我们提出了一种带有人工反馈的弱监督学习，旨在同时实现高精度的分割和高效的注释。在两个基准数据集上进行评估，我们的方法达到了与完全监督方法相当的高质量细胞分割结果，但需要的人工注释工作量要少得多。
