NormToRaw: A Style Transfer Based Self-supervised Learning Approach for Nuclei Segmentation

Nuclei segmentation is valuable in histopathological image analysis, but labeling nuclei is costly. Different organs, pa-tients and diseases will lead to high variability in the morphology of nuclei, the structure of tissues, etc., which is difficult to elim-inate. Inconsistent staining operations and scanning operations will cause variability in histopathological image style. Relying on a small amount of labeled data, it is hard for the model to adapt to the high variability among histopathological images. Therefore, it is necessary to exploit the value in the massive unlabeled data. However, because the existing pretext tasks in self-supervised learning do not well consider the characteristics of histopathological images and segmentation task, the same for the existing data augmentation approaches in contrastive learning, they are not suitable for nuclei segmentation. In this paper, the proposed method, named NormToRaw, takes into consideration the characteristics of nuclei segmentation, which can learn semantic information from different stains by style transfer. A generative adversarial network is used to transfer the normalized image to the raw image. Pre-trained on more than 8,000 unlabeled images and trained on 16 labeled images, the experimental results of 5 pre-trained models showed that the proposed method is effective for improving the performance of nuclei segmentation.