Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
This addresses the challenge of data scarcity in 3D microscopy segmentation for biological research, though it is incremental as it builds on existing adversarial network methods.
The paper tackles the problem of requiring large manually annotated datasets for 3D nuclei segmentation in fluorescence microscopy by using synthetic 3D volumes generated with spatially constrained cycle-consistent adversarial networks for training, achieving successful segmentation across various datasets.
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.