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SAGE-GAN: Towards Realistic and Robust Segmentation of Spatially Ordered Nanoparticles via Attention-Guided GANs

Anindya Pal, Varun Ajith, Saumik Bhattacharya, Sayantari Ghosh

Abstract

Precise analysis of nanoparticles for characterization in electron microscopy images is essential for advancing nanomaterial development. Yet it remains challenging due to the time-consuming nature of manual methods and the shortcomings of traditional automated segmentation techniques, especially when dealing with complex shapes and imaging artifacts. While conventional methods yield promising results, they depend on a large volume of labeled training data, which is both difficult to acquire and highly time-consuming to generate. In order to overcome these challenges, we have developed a two-step solution: Firstly, our system learns to segment the key features of nanoparticles from a dataset of real images using a self-attention driven U-Net architecture that focuses on important physical and morphological details while ignoring background features and noise. Secondly, this trained Attention U-Net is embedded in a cycle-consistent generative adversarial network (CycleGAN) framework, inspired by the cGAN-Seg model introduced by Abzargar et al. This integration allows for the creation of highly realistic synthetic electron microscopy image-mask pairs that naturally reflect the structural patterns learned by the Attention U-Net. Consequently, the model can accurately detect features in a diverse array of real-world nanoparticle images and autonomously augment the training dataset without requiring human input. Cycle consistency enforces a direct correspondence between synthetic images and ground-truth masks, ensuring realistic features, which is crucial for accurate segmentation training.

SAGE-GAN: Towards Realistic and Robust Segmentation of Spatially Ordered Nanoparticles via Attention-Guided GANs

Abstract

Precise analysis of nanoparticles for characterization in electron microscopy images is essential for advancing nanomaterial development. Yet it remains challenging due to the time-consuming nature of manual methods and the shortcomings of traditional automated segmentation techniques, especially when dealing with complex shapes and imaging artifacts. While conventional methods yield promising results, they depend on a large volume of labeled training data, which is both difficult to acquire and highly time-consuming to generate. In order to overcome these challenges, we have developed a two-step solution: Firstly, our system learns to segment the key features of nanoparticles from a dataset of real images using a self-attention driven U-Net architecture that focuses on important physical and morphological details while ignoring background features and noise. Secondly, this trained Attention U-Net is embedded in a cycle-consistent generative adversarial network (CycleGAN) framework, inspired by the cGAN-Seg model introduced by Abzargar et al. This integration allows for the creation of highly realistic synthetic electron microscopy image-mask pairs that naturally reflect the structural patterns learned by the Attention U-Net. Consequently, the model can accurately detect features in a diverse array of real-world nanoparticle images and autonomously augment the training dataset without requiring human input. Cycle consistency enforces a direct correspondence between synthetic images and ground-truth masks, ensuring realistic features, which is crucial for accurate segmentation training.

Paper Structure

This paper contains 19 sections, 24 equations, 14 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Attention U-Net
  4. CycleGAN
  5. Style-Based U-Net Generator
  6. Training Workflow
  7. Loss Functions
  8. Dataset and Pre-processing
  9. Dataset Description
  10. Training and Validation Split
  11. Dataset Preprocessing
  12. Exclusion of Synthetic Data
  13. Evaluation Metrics
  14. Experimental Results
  15. Quantitative Performance Evaluation
  16. ...and 4 more sections

Figures (14)

  • Figure 1: Schematic of the proposed two phase pipeline. Phase one depicts the pretraining of the Attention U-Net architecture and Phase two represents the training of the CycleGAN framework with the pretrained U-Net embedded within the architecture. The broken arrows indicate the cycle consistency between the generator and the segmentation network.
  • Figure 2: Attention U-Net
  • Figure 3: Cycle GAN
  • Figure 4: Image
  • Figure 5: Mask
  • ...and 9 more figures