HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization (2412.20924v1)

Abstract: Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid the expensive and laborious acquisition of pixel-level annotations, a wide range of studies attempt to adopt the class activation map (CAM), a weakly-supervised learning scheme, to achieve pixel-level tissue segmentation. However, CAM-based methods are prone to suffer from under-activation and over-activation issues, leading to poor segmentation performance. To address this problem, we propose a novel weakly-supervised semantic segmentation framework for histopathological images based on image-mixing synthesis and consistency regularization, dubbed HisynSeg. Specifically, synthesized histopathological images with pixel-level masks are generated for fully-supervised model training, where two synthesis strategies are proposed based on Mosaic transformation and B\'ezier mask generation. Besides, an image filtering module is developed to guarantee the authenticity of the synthesized images. In order to further avoid the model overfitting to the occasional synthesis artifacts, we additionally propose a novel self-supervised consistency regularization, which enables the real images without segmentation masks to supervise the training of the segmentation model. By integrating the proposed techniques, the HisynSeg framework successfully transforms the weakly-supervised semantic segmentation problem into a fully-supervised one, greatly improving the segmentation accuracy. Experimental results on three datasets prove that the proposed method achieves a state-of-the-art performance. Code is available at https://github.com/Vison307/HisynSeg.

• The paper introduces HisynSeg, which leverages novel image-mixing synthesis and consistency regularization to overcome limitations of CAM-based segmentation.
• Its synthesized image filtering module ensures high-quality training samples by discarding artifacts from staining variations and resolution differences.
• Experimental results demonstrate a 2% mIoU improvement on WSSS4LUAD, highlighting robust performance with minimal pixel-level annotation.

Overview of HisynSeg: A Weakly-Supervised Framework for Histopathological Image Segmentation

The paper presents HisynSeg, a novel framework for weakly-supervised histopathological image segmentation, which addresses the prevalent challenges in utilizing Class Activation Maps (CAMs) for this purpose. The proposed method innovatively combines image-mixing synthesis with consistency regularization, transcending the traditional limitations associated with CAM-based approaches that often suffer from under-activation and over-activation issues. This essay will provide an analysis of the methods employed in HisynSeg, discuss significant results, and speculate on its implications in the field of computational pathology.

Methodological Contributions

The proposed framework, HisynSeg, is structured around three primary components: the image-mixing synthesis module, synthesized image filtering module, and histopathological image segmentation module. Here's a detailed exploration of these components:

1. Image-Mixing Synthesis Module: HisynSeg introduces a novel synthesis approach leveraging Mosaic transformations and Bézier mask generation. Mosaic transformations create synthesized images by combining multiple single-tissue images, while Bézier masks adopt smooth, curved boundaries, resulting in synthesized images that closely mimic real histopathological images. This approach effectively bridges the conceptual gap between weakly-supervised and fully-supervised methodologies by providing pixel-level accurate masks for the generated images.
2. Synthesized Image Filtering Module: Given the proclivity for artifacts in synthesized images due to staining variations and differing image resolutions, an image discriminator is employed. This module discards synthesized images that lack authenticity, ensuring only credible images are used for training. This step is crucial to avoid model overfitting to artifacts.
3. Histopathological Image Segmentation Module: HisynSeg applies a self-supervised consistency regularization technique on real, unmasked images to incorporate them into model training beneficially. This consistency regularization aligns the outputs of the segmentation model with the auxiliary classifier, thereby utilizing image-level labels effectively without requiring pixel annotation.

Experimental Results and Analysis

The authors evaluated HisynSeg on three distinct datasets: WSSS4LUAD, BCSS-WSSS, and LUAD-HistoSeg. The experimental results demonstrate HisynSeg's superior performance compared to existing methods. Notably, HisynSeg achieved state-of-the-art performance across all major metrics — mIoU and fwIoU in particular — underscoring its enhanced segmentation accuracy. In the case of the WSSS4LUAD dataset, HisynSeg improved the mIoU by 2% compared to previous methods, which is indicative of its performance gains in segmentation tasks.

The effectiveness of HisynSeg's strategies was further validated through ablation studies that highlighted the critical role of the synthesized image filtering module and consistency regularization in achieving robust segmentation performance. The ability to maintain performance even with a limited number of training samples further underscores the versatility and effectiveness of the synthesis strategies.

Implications and Speculations

The introduction of HisynSeg presents noteworthy implications for computational pathology and other fields relying on histopathological image analysis. Its ability to dispense with the necessity for extensive pixel-level annotation while maintaining high segmentation accuracy offers significant practical advantages in clinical settings where expert annotation is costly and time-consuming.

Theoretically, HisynSeg challenges conventional paradigms in weakly-supervised learning by demonstrating the efficacy of synthesizing annotations through image-mixing, suggesting potential broader applications. Future developments might see the convergence of generative models with HisynSeg's framework to enhance the realism of synthesized images further or adapt similar methodologies for other medical imaging tasks.

In conclusion, HisynSeg represents an important advancement in weakly-supervised semantic segmentation of histopathological images, offering a scalable approach to circumvent the limitations of current methods. Its innovative use of image synthesis and unsupervised regularization sets a benchmark for future research, with the potential to inspire new avenues in AI-assisted medical imaging and beyond.