A Conditional Generative Framework for Synthetic Data Augmentation in Segmenting Thin and Elongated Structures in Biological Images

• URL: http://arxiv.org/abs/2512.10334v1
• Date: Thu, 11 Dec 2025 06:36:44 GMT
• Title: A Conditional Generative Framework for Synthetic Data Augmentation in Segmenting Thin and Elongated Structures in Biological Images
• Authors: Yi Liu, Yichi Zhang,
• Abstract summary: We propose a conditional generative framework to generate realistic filaments in microscopy images from binary masks.<n>We also propose a filament-aware structural loss to improve the structure similarity when generating synthetic images.
• Score: 8.599003203525731
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Thin and elongated filamentous structures, such as microtubules and actin filaments, often play important roles in biological systems. Segmenting these filaments in biological images is a fundamental step for quantitative analysis. Recent advances in deep learning have significantly improved the performance of filament segmentation. However, there is a big challenge in acquiring high quality pixel-level annotated dataset for filamentous structures, as the dense distribution and geometric properties of filaments making manual annotation extremely laborious and time-consuming. To address the data shortage problem, we propose a conditional generative framework based on the Pix2Pix architecture to generate realistic filaments in microscopy images from binary masks. We also propose a filament-aware structural loss to improve the structure similarity when generating synthetic images. Our experiments have demonstrated the effectiveness of our approach and outperformed existing model trained without synthetic data.

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