3D fluorescence microscopy data synthesis for segmentation and benchmarking
This addresses the problem of data scarcity for researchers in biomedical imaging, though it is incremental as it builds on existing generative and simulation techniques.
The paper tackles the lack of annotated training data for deep learning in 3D fluorescence microscopy by proposing a method using conditional generative adversarial networks to generate realistic image data from annotation masks, resulting in publicly available fully-annotated datasets for training or benchmarking.
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.