轻量CycleGAN实现荧光显微图像模态转换与实验质量诊断
Lightweight CycleGAN Models for Cross-Modality Image Transformation and Experimental Quality Assessment in Fluorescence Microscopy
- 用固定通道替代传统扩张策略，参数从4180万降至约9000个
- 在无配对数据下完成共聚焦到超分辨显微图像转换，训练更快内存更少
- 生成结果可识别光漂白、伪影等实验问题，适合科研图像质量验证
轻量级深度学习模型能显著降低计算成本和环境影响，对科学应用至关重要。本文提出一种用于荧光显微镜模态转换（共聚焦到超分辨率STED/去卷积STED）的轻量级CycleGAN，解决了无配对数据的常见挑战。通过将基于U-Net的生成器中传统的通道翻倍策略替换为固定通道设计，可将可训练参数从4180万大幅减少至约9000个，在实现更优性能的同时，训练速度更快、内存占用更低。此外，我们引入GAN作为实验与标记质量的诊断工具：当在高质量图像上训练后，该模型能学习最优成像特征；其生成输出与新实验图像之间的偏差，可揭示光漂白、伪影或标记不准等问题。这使模型成为显微镜工作流中验证实验准确性和图像保真度的实用工具。
原文摘要 · Abstract (English)
Lightweight deep learning models offer substantial reductions in computational cost and environmental impact, making them crucial for scientific applications. We present a lightweight CycleGAN for modality transfer in fluorescence microscopy (confocal to super-resolution STED/deconvolved STED), addressing the common challenge of unpaired datasets. By replacing the traditional channel-doubling strategy in the U-Net-based generator with a fixed channel approach, we drastically reduce trainable parameters from 41.8 million to approximately nine thousand, achieving superior performance with faster training and lower memory usage. We also introduce the GAN as a diagnostic tool for experimental and labeling quality. When trained on high-quality images, the GAN learns the characteristics of optimal imaging; deviations between its generated outputs and new experimental images can reveal issues such as photobleaching, artifacts, or inaccurate labeling. This establishes the model as a practical tool for validating experimental accuracy and image fidelity in microscopy workflows.
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