用自监督方法提升生物显微图像的三维分辨率，效果优于现有技术。
Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation
- 通过非线性插值连续增强切片间分辨率，每轮翻倍
- 在多种显微成像模式下均超越现有方法，速度与精度兼顾
- 支持任意间距插值，适合不均匀采样数据，开源易用
三维生物显微成像极大推动了对复杂生物结构的理解。然而，由于显微技术、样本特性或光毒性限制，常导致z轴分辨率低，影响细胞测量准确性。本文提出ZAugNet，一种快速、准确且自监督的深度学习方法，用于提升生物图像的z轴分辨率。通过在相邻切片间进行非线性插值，ZAugNet每轮迭代可使分辨率翻倍。在多种显微模态和生物样本上评估，其在多数指标上优于现有方法。该方法结合生成对抗网络（GAN）架构与知识蒸馏，实现高速预测而不牺牲精度。此外，我们开发了ZAugNet+，支持任意距离的连续插值，特别适用于非均匀切片间距的数据集。两者均为高性能、可扩展的三维切片增强方案，以PyTorch开源，配套直观的Colab笔记本，便于科研社区使用。
原文摘要 · Abstract (English)
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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