Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images

Cell segmentation is usually the first step for downstream single-cell analysis in microscopy image-based biology and biomedical research. Deep learning has been widely used for image segmentation, but it is hard to collect a large number of labelled cell images to train models because manually annotating cells is extremely time-consuming and costly. Furthermore, datasets used are often limited to one modality and lacking in diversity, leading to poor generalization of trained models. This competition aims to benchmark cell segmentation methods that could be applied to various microscopy images across multiple imaging platforms and tissue types. We frame the cell segmentation problem as a weakly supervised learning task to encourage models that use limited labelled and many unlabelled images for cell segmentation as unlabelled images are relatively easy to obtain in practice. We will implement a U-Net model as a baseline owing to their established success in biomedical image segmentation. This competition could serve as an important step toward universal and fully automatic cell image analysis tools, greatly accelerating the rate of discovery from image-based biological and biomedical research.