Fu, ChichenLee, SoonamHo, David JoonHan, ShuoSalama, PaulDunn, Kenneth W.Delp, Edward J.2019-11-152019-11-152018-06Fu, C., Lee, S., Ho, D. J., Han, S., Salama, P., Dunn, K. W., & Delp, E. J. (2018). Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2302–23028. https://doi.org/10.1109/CVPRW.2018.00298https://hdl.handle.net/1805/21351Advances 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.enPublisher Policythree-dimensional displaysimage segmentationmicroscopyConference proceedings