SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
This addresses the need for high-quality synthetic histopathology images to enhance medical image analysis, though it is incremental as it builds on existing normalization and synthesis techniques.
The paper tackled the problem of generating realistic histopathology images for multiple organs, which existing methods failed to align with organ-specific styles and produce accurate nuclei boundaries, resulting in a method that outperforms four state-of-the-art approaches across five organs and improves instance segmentation to state-of-the-art performance.
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.