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arXiv · 1801.07198

Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

Chichen Fu·Soonam Lee·David Joon Ho·Shuo Han·Paul Salama·Kenneth W. Dunn·Edward J. Delp
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Abstract

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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Chichen Fu, Soonam Lee, David Joon Ho, Shuo Han, Paul Salama, Kenneth W. Dunn, Edward J. Delp. 2018-01-22. Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation. https://doi.org/10.1109/cvprw.2018.00298

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