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; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2221-2229

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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Fu_2018_CVPR_Workshops,
author = {Fu, Chichen and Lee, Soonam and Joon Ho, David and Han, Shuo and Salama, Paul and Dunn, Kenneth W. and Delp, Edward J.},
title = {Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}