  Nucleus segmentation is usually the first step in pathological image analysis
tasks. Generalizable nucleus segmentation refers to the problem of training a
segmentation model that is robust to domain gaps between the source and target
domains. The domain gaps are usually believed to be caused by the varied image
acquisition conditions, e.g., different scanners, tissues, or staining
protocols. In this paper, we argue that domain gaps can also be caused by
different foreground (nucleus)-background ratios, as this ratio significantly
affects feature statistics that are critical to normalization layers. We
propose a Distribution-Aware Re-Coloring (DARC) model that handles the above
challenges from two perspectives. First, we introduce a re-coloring method that
relieves dramatic image color variations between different domains. Second, we
propose a new instance normalization method that is robust to the variation in
foreground-background ratios. We evaluate the proposed methods on two H$\&$E
stained image datasets, named CoNSeP and CPM17, and two IHC stained image
datasets, called DeepLIIF and BC-DeepLIIF. Extensive experimental results
justify the effectiveness of our proposed DARC model. Codes are available at
\url{https://github.com/csccsccsccsc/DARC
