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

2025-12-11

AI Summary

To address the scarcity of high-quality pixel-level annotations for segmenting elongated fibrous structures—such as microtubules and actin—in biological images, this paper proposes a fiber-aware conditional generative adversarial framework. Built upon the Pix2Pix architecture, it is the first to adapt this GAN paradigm for controllable synthesis of slender biological structures. We introduce a structural loss function that jointly enforces skeleton consistency and orientation sensitivity, and incorporate microscopy-specific image priors to enable end-to-end training. Extensive evaluation across multiple biological datasets demonstrates that segmentation models trained on our synthetic data achieve a 4.2% improvement in mDice over the unenhanced baseline. Moreover, synthesized fibers attain 92% morphological similarity to ground-truth annotations, as quantified by standard metrics. This work establishes a generalizable, low-annotation-dependency data augmentation paradigm for segmenting elongated biological structures.

Technology Category

Computer Vision: Generative Adversarial Networks (GANs) for VisionNatural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Deep Generative Models & Autoencoders

Application Category

Web Mining and Content Analysis: Web data generation and simulationEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Segmenting thin, elongated filaments in biological images for analysis.
• Addressing the shortage of high-quality annotated filament datasets.
• Generating realistic synthetic filament images to improve segmentation models.

Innovation

• Conditional generative framework for synthetic data generation
• Filament-aware structural loss enhances structure similarity
• Pix2Pix architecture generates realistic filaments from masks