A Weakly Supervised Method With Colorization for Nuclei Segmentation Using Point Annotations

Nuclei segmentation is an essential step in the automatic analysis of histopathology images. This segmentation task requires much work to manually generate accurate pixel-level annotations for fully supervised training. To overcome such expensive and hard-to-get pixel-level annotations, in this article, we propose a weakly supervised method for nuclei segmentation only using point annotations based on convolutional neural network (CNN). The proposed method effectively combines a weakly supervised segmentation task and an auxiliary colorization task. The dual input with boundaries and color information maximizes the inherent features of the image. Two types of coarse labels generated from point annotations are applied to provide constraint information for the segmentation task. As an auxiliary task, colorization is incorporated to guide the network to extract effective features and improve the segmentation performance of the network. We evaluate our proposed method on two public nuclei segmentation datasets. The experimental results indicate that our method is superior to other state-of-the-art methods.

Automated summary

Nuclei segmentation is performed using a weakly supervised method based on convolutional neural network and point annotations. The proposed method maximizes the image's inherent features through dual input with boundaries and color information. Two types of coarse labels generated from point annotations provide constraint information for the segmentation task. Incorporating colorization as an auxiliary task improves network performance by guiding the extraction of effective features. Experimental results demonstrate the superiority of the proposed method over other state-of-the-art methods.