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

TL;DR

This paper introduces a conditional generative framework using Pix2Pix to create synthetic filamentous structures in microscopy images, enhancing segmentation performance amid limited annotated data.

Contribution

The authors develop a novel filament-aware structural loss and a Pix2Pix-based method for generating realistic synthetic biological filaments to augment training data.

Findings

Synthetic data improved segmentation accuracy.

The approach outperformed models trained without synthetic data.

Generated filaments closely resembled real structures.

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.