Nuclear Pore Segmentation in 3D FIB-SEM Images with Dynamic Cyclical Data Augmentation

We developed a deep learning approach to detect and segment nuclear pores in large high-resolution 3D FIB-SEM images. The approach extracts small blocks containing nuclear pore regions from different parts of a cell nucleus and applies data augmentation techniques combined with Random Block Sampling (RBS) to optimize training. By fine tuning the neural network and reducing the batch size to one, we enhanced the model’s ability to learn from individual image features, leading to more precise detection. Our Dynamic Cyclical Data Augmentation (DCDA) adapts in real-time with an automatic termination feature, reducing augmentation time by over 97.4% in our experiments, significantly improving efficiency without compromising model performance. Experimental results consistently demonstrate that our approach can quickly and accurately identify nuclear pores, achieving F1 scores from 0.7119 to 0.8555, with precision ranging from 0.7379 to 0.8516. Training time was also significantly reduced. These substantial improvements in both accuracy and efficiency underscore the method’s effectiveness for large-scale cellular analysis.