Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

Preprint English OPEN
Fu, Chichen ; Lee, Soonam ; Ho, David Joon ; Han, Shuo ; Salama, Paul ; Dunn, Kenneth W. ; Delp, Edward J. (2018)
  • Related identifiers: doi: 10.1109/CVPRW.2018.00298
  • Subject: Computer Science - Computer Vision and Pattern Recognition
    acm: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

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.
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