Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
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.
Cite
Text
Fu et al. "Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00298Markdown
[Fu et al. "Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/fu2018cvprw-three/) doi:10.1109/CVPRW.2018.00298BibTeX
@inproceedings{fu2018cvprw-three,
title = {{Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation}},
author = {Fu, Chichen and Lee, Soonam and Ho, David Joon and Han, Shuo and Salama, Paul and Dunn, Kenneth W. and Delp, Edward J.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {2221-2229},
doi = {10.1109/CVPRW.2018.00298},
url = {https://mlanthology.org/cvprw/2018/fu2018cvprw-three/}
}