Nuclei Segmentation Using Mixed Points and Masks Selected from Uncertainty

Weakly supervised learning has drawn much attention to mitigate the manual effort of annotating pixel-level labels for segmentation tasks. In nuclei segmentation, point annotation has been successfully used for training. However, points lack the shape information. Thus the segmentation of nuclei with non-uniform color is unsatisfactory. In this paper, we propose a framework of weakly supervised nuclei segmentation using mixed points and masks annotation. To save the extra annotation effort, we select typical nuclei to annotate masks from uncertainty map. Using Bayesian deep learning tools, we first train a model with points annotation to predict the uncertainty. Then we utilize the uncertainty map to select the representative hard nuclei for mask annotation automatically. The selected nuclear masks are combined with points to train a better segmentation model. Experimental results on two nuclei segmentation datasets prove the effectiveness of our method. The code is publicly available.