Res2-Unet: An Enhanced Network for Generalized Nuclear Segmentation in Pathological Images

The morphology of nuclei in a pathological image plays an essential role in deriving high-quality diagnosis to pathologists. Recently, deep learning techniques have pushed forward this field significantly in the generalization ability, i.e., segmenting nuclei from different patients and organs by using the same CNN model. However, it remains challenging to design an effective network that segments nuclei accurately, due to their diverse color and morphological appearances, nuclei touching or overlapping, etc. In this paper, we propose a novel network named Res2-Unet to relief this problem. Res2-Unet inherits the contracting-expansive structure of U-Net. It is featured by employing advanced network modules such as the residual and squeeze-and-excitation (SE) to enhance the segmentation capability. The residual module is utilized in both contracting and expansive paths for comprehensive feature extraction and fusion, respectively. While the SE module enables selective feature propagation between the two paths. We evaluate Res2-Unet on two public nuclei segmentation benchmarks. The experiments show that by equipping the modules individually and jointly, performance gains are consistently observed compared to the baseline and several existing methods.