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

翻译：细胞核分割通常是病理图像分析任务中的第一步。可泛化的细胞核分割指的是训练一个分割模型，使其对源域与目标域之间的域差异具有鲁棒性。通常认为域差异是由不同的图像采集条件（例如不同的扫描仪、组织类型或染色方案）引起的。本文提出，域差异也可能由不同的前景（细胞核）-背景比例引起，因为该比例显著影响对归一化层至关重要的特征统计量。我们提出了一种分布感知重着色（DARC）模型，从两个角度处理上述挑战。首先，我们引入了一种重着色方法，以缓解不同域之间剧烈的图像颜色变化。其次，我们提出了一种新的实例归一化方法，该方法对前景-背景比例的变化具有鲁棒性。我们在两个H&E染色图像数据集（命名为CoNSeP和CPM17）以及两个IHC染色图像数据集（命名为DeepLIIF和BC-DeepLIIF）上评估了所提出的方法。大量实验结果证明了我们提出的DARC模型的有效性。代码可在\url{https://github.com/csccsccsccsc/DARC}获取。