Instance segmentation of biological images using graph convolutional network

Instance segmentation in biological images is an important task in the field of biological images and biomedical analysis. Different from the instance segmentation of natural image scenes, this task is still challenging because there are a large number of overlapping objects with similar appearance as well as great variability in shape, size and texture in the foreground and background. In this paper, we propose a novel method for segmentation of graph-guided instances of biological images, which successfully addresses these peculiarities. Our method predicts the embedding at each pixel and uses clustering to recover instances during testing. Specifically, we design the Graph-guided Feature Fusion Module in response to overlapping instances. Our Graph-guided Feature Fusion Module combines fine deep features and coarse shallow features to learn the affinity matrix, and then uses graph convolutional network to guide the network to learn object-level local features. Next, we devise the Gated Spatial Attention Module to effectively learn key spatial information by introducing a gating mechanism. Furthermore, we give the Cluster Distance Loss that can effectively distinguish foreground objects from similar backgrounds. The effectiveness of our proposed method has been verified on various biological and biomedical datasets. The experimental results show that our method is superior to previous embedding-based instance segmentation methods. The SBD metric for our method reached 90.8% on the plant phenotype dataset (CVPPP), 72.5% on the cell nucleus dataset (DSB2018), and 81.8% on the C.elegans dataset, all achieving state-of-the-art performance.

Automated summary

Instance segmentation in biological images is a crucial task in the field of biological images and biomedical analysis. This paper proposes a novel method for segmentation of graph-guided instances in biological images. The method combines graph-guided feature fusion and gated spatial attention modules, and introduces a cluster distance loss to effectively distinguish foreground objects from similar backgrounds.