Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
Three-dimensional biological microscopy has significantly advanced our
understanding of complex biological structures. However, limitations due to
microscopy techniques, sample properties or phototoxicity often result in poor
z-resolution, hindering accurate cellular measurements. Here, we introduce
ZAugNet, a fast, accurate, and self-supervised deep learning method for
enhancing z-resolution in biological images. By performing nonlinear
interpolation between consecutive slices, ZAugNet effectively doubles
resolution with each iteration. Compared on several microscopy modalities and
biological objects, it outperforms competing methods on most metrics. Our
method leverages a generative adversarial network (GAN) architecture combined
with knowledge distillation to maximize prediction speed without compromising
accuracy. We also developed ZAugNet+, an extended version enabling continuous
interpolation at arbitrary distances, making it particularly useful for
datasets with nonuniform slice spacing. Both ZAugNet and ZAugNet+ provide
high-performance, scalable z-slice augmentation solutions for large-scale 3D
imaging. They are available as open-source frameworks in PyTorch, with an
intuitive Colab notebook interface for easy access by the scientific community.
