SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
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DOI：
10.1109/isbi53787.2023.10230507
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Haotian Wang;Min Xian;Aleksandar Vakanski;Bryar Shareef
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作者：
Haotian Wang;Min Xian;Aleksandar Vakanski;Bryar Shareef
现有的用于组织病理学图像合成的深度神经网络无法生成与不同器官对齐的图像风格，并且无法产生簇核的准确边界。为了解决这些问题，我们提出了一种风格引导的实例自适应归一化（SIAN）方法来合成来自不同器官的组织病理学图像的真实颜色分布和纹理。 SIAN 包含四个阶段：语义化、风格化、实例化和调制。前两个阶段通过使用语义图和学习的图像风格向量来综合图像语义和风格。实例化模块集成几何和拓扑信息并生成准确的原子核边界。我们在多器官数据集上验证了所提出的方法，广泛的实验结果表明，所提出的方法比五个器官的四种最先进的方法生成更真实的组织病理学图像。通过将所提出的模型训练方法中的合成图像结合起来，实例分割网络可以实现最先进的性能。
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.