Existing deep neural networks for histopathology image synthesis cannot generate image styles that align with different organs, and cannot produce accurate boundaries of clustered nuclei. To address these issues, we propose a style-guided instance-adaptive normalization (SIAN) approach to synthesize realistic color distributions and textures for histopathology images from different organs. SIAN contains four phases, semantization, stylization, instantiation, and modulation. The first two phases synthesize image semantics and styles by using semantic maps and learned image style vectors. The instantiation module integrates geometrical and topological information and generates accurate nuclei boundaries. We validate the proposed approach on a multiple-organ dataset, Extensive experimental results demonstrate that the proposed method generates more realistic histopathology images than four state-of-the-art approaches for five organs. By incorporating synthetic images from the proposed approach to model training, an instance segmentation network can achieve state-of-the-art performance.

翻译：为了解决这些问题,我们提议采用一种风格引导的对地对地调整常规(SIAN)方法,以综合不同器官的对地貌图象的现实颜色分布和纹理。SIAN包含四个阶段,即精密化、立体化、即时化和调制。前两个阶段,通过使用语义图和有学识的图像风格矢量,合成图像语义和风格。即时化模块将几何学和地形学信息整合在一起,并生成准确的核界。我们验证了多机数据集的拟议方法,广泛的实验结果表明,拟议方法为五种器官生成的对地貌图象比四种最先进的方法更现实。通过将拟议方法的合成图像纳入模型培训,实例分割网络可以实现最先进的性能。