Three-Dimensional Nuclei Synthesis for Fluorescence Microscopy Image Analysis

Three-dimensional tissue cytometry is an important technique for quantitative analysis of cell structures in large fluorescence microscopy volumes. Accurate nuclei detection and segmentation is an important step for 3D tissue cytometry. Deep learning methods have shown promising results for nuclei detection and segmentation. However, manually annotating ground truth for training deep learning methods is labor-intensive and not practical for large 3D volumes. In this paper, we propose a 3D nuclei synthesis method, known as 3DSpCycleGAN, for generating 3D ground truth volumes along with corresponding synthetic microscopy volumes. Experimental results using fluorescence microscopy volumes demonstrate that our method generates more realistic 3D volumes when evaluated both visually and quantitatively than previously reported. We also show that using the synthetic volumes generated by 3DSpCycleGAN as training data improves segmentation accuracy for deep learning segmentation techniques.