Weakly Supervised Cell-Instance Segmentation with Two Types of Weak Labels by Single Instance Pasting

Kazuya Nishimura, Ryoma Bise

Abstract: Cell instance segmentation that recognizes each cell boundary is an important task in cell image analysis. While deep learning-based methods have shown promising performances with a certain amount of training data, most of them require full annotations that show the boundary of each cell. Generating the annotation for cell segmentation is time-consuming and human labor. To reduce the annotation cost, we propose a weakly supervised segmentation method using two types of weak labels (one for cell type and one for nuclei position). Unlike general images, these two labels are easily obtained in phase-contrast images. The intercellular boundary, which is necessary for cell instance segmentation, cannot be directly obtained from these two weak labels, so to generate the boundary information, we propose a single instance pasting based on the copy-and-paste technique. First, we locate single-cell regions by counting cells and store them in a pool. Then, we generate the intercel-lular boundary by pasting the stored single-cell regions to the original image. Finally, we train a boundary estimation network with the generated labels and perform instance segmentation with the network. Our evaluation on a public dataset demonstrated that the proposed method achieves the best performance among the several weakly supervised methods we compared.