3.8 Proceedings Paper

InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation

出版社

SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-16434-7_14

关键词

Data augmentation; Morphology constraints; Generative

资金

  1. Beijing Institute of Collaborative Innovation Funding [BICI22EG01]
  2. HKSAR RGC General Research Fund (GRF) [16203319]

向作者/读者索取更多资源

This paper proposes a realistic data augmentation method called InsMix for nuclei segmentation. The method utilizes morphology constraints and background perturbation to enhance the images, enabling rich information acquisition about the nuclei while preserving their morphology characteristics. Experimental results demonstrate the superior performance of the proposed method on two datasets.
Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth principle and performs morphology-constrained generative instance augmentation. Specifically, we propose morphology constraints that enable the augmented images to acquire luxuriant information about nuclei while maintaining their morphology characteristics (e.g., geometry and location). To fully exploit the pixel redundancy of the background and improve the model's robustness, we further propose a background perturbation method, which randomly shuffles the background patches without disordering the original nuclei distribution. To achieve contextual consistency between original and template instances, a smooth-GAN is designed with a foreground similarity encoder (FSE) and a triplet loss. We validated the proposed method on two datasets, i.e., Kumar and CPS datasets. Experimental results demonstrate the effectiveness of each component and the superior performance achieved by our method to the state-of-the-art methods.The source code is available at https://github.com/hust-linyi/insmix.

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