PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting

2025-03-06

AI Summary

To address the high annotation cost and scarcity of high-quality image-mask pairs in pathological tumor segmentation, this paper proposes a tumor-region-aware background-preserving image inpainting framework. Methodologically, it introduces a novel tumor-conditioned region embedding inpainting mechanism, integrated with cross-image region-guided sampling and uncertainty-aware synthetic region confidence filtering—ensuring biological plausibility while enhancing synthetic data diversity and pixel-level annotation accuracy. Additionally, multi-scale adaptive training is incorporated to improve generalization. Evaluated on multiple benchmarks including CAMELYON16, the method significantly outperforms existing synthesis approaches (achieving a +2.00% Dice score improvement), with particularly pronounced gains in low-shot settings. This work establishes an efficient and reliable data augmentation paradigm for resource-constrained pathological image segmentation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained image-mask pairs annotated by pathologists. Thus, synthesizing histopathology data to expand the dataset is highly desirable. Previous works suffer from inaccuracies and limited diversity in image-mask pairs, both of which affect training segmentation, particularly in small-scale datasets and the inherently complex nature of histopathology images. To address this challenge, we propose PathoPainter, which reformulates image-mask pair generation as a tumor inpainting task. Specifically, our approach preserves the background while inpainting the tumor region, ensuring precise alignment between the generated image and its corresponding mask. To enhance dataset diversity while maintaining biological plausibility, we incorporate a sampling mechanism that conditions tumor inpainting on regional embeddings from a different image. Additionally, we introduce a filtering strategy to exclude uncertain synthetic regions, further improving the quality of the generated data. Our comprehensive evaluation spans multiple datasets featuring diverse tumor types and various training data scales. As a result, segmentation improved significantly with our synthetic data, surpassing existing segmentation data synthesis approaches, e.g., 75.69% ->77.69% on CAMELYON16. The code is available at https://github.com/HongLiuuuuu/PathoPainter.

Problem

• Enhances histopathology tumor segmentation accuracy
• Generates diverse, biologically plausible synthetic image-mask pairs
• Improves segmentation performance on small-scale datasets

Innovation

• Tumor-aware inpainting for precise image-mask alignment
• Regional embeddings enhance dataset diversity plausibly
• Filtering strategy improves synthetic data quality