Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation

• URL: http://arxiv.org/abs/2503.04843v2
• Date: Mon, 17 Mar 2025 21:52:46 GMT
• Title: Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation
• Authors: Alessandro Pasqui, Sajjad Mahdavi, Benoit Vianay, Alexandra Colin, Alex McDougall, Rémi Dumollard, Yekaterina A. Miroshnikova, Elsa Labrune, Hervé Turlier,
• Abstract summary: ZAugNet is a fast, accurate, and self-supervised deep learning method for enhancing z-resolution in biological images.<n>By performing nonlinear distances between consecutive slices, ZAugNet effectively doubles resolution with each iteration.<n>ZAugNet+ is an extended version enabling continuous prediction at arbitrary distances.
• Score: 65.46249968484794
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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