Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany.
PLoS One. 2021 Dec 2;16(12):e0260509. doi: 10.1371/journal.pone.0260509. eCollection 2021.
Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures. In combination with mask simulation approaches, we demonstrate the generation of fully-annotated 3D microscopy data sets that we make publicly available for training or benchmarking. An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics and allows to generate image data of different quality levels. A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms. We present this as a proof-of-concept for the automated generation of fully-annotated training data sets requiring only a minimum of manual interaction to alleviate the need of manual annotations.
自动化图像处理方法对于许多生物医学实验是不可或缺的，有助于以快速和可重复的方式处理不断增加的显微镜图像数据。特别是最先进的基于深度学习的方法通常需要大量带注释的训练数据来生成准确和通用的输出，但它们通常受到缺乏这些带注释数据集的限制。在这项工作中，我们提出了如何利用条件生成对抗网络从 3D 细胞结构的注释掩模生成用于 3D 荧光显微镜的逼真图像数据。结合掩模模拟方法，我们展示了完全注释的 3D 显微镜数据集的生成，我们将其公开提供用于训练或基准测试。对细胞结构进行附加的位置条件处理，可以重建与位置相关的强度特征，并允许生成不同质量水平的图像数据。使用逐块工作原理和随后的全尺寸重新组装策略，可以生成任意大小和不同生物体的图像数据。我们将其作为自动化生成完全注释的训练数据集的概念验证，仅需要最少的人工交互来减轻手动注释的需求。
