Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks
Abstract
Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D.
Cite
Text
Ho et al. "Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.116Markdown
[Ho et al. "Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/ho2017cvprw-nuclei/) doi:10.1109/CVPRW.2017.116BibTeX
@inproceedings{ho2017cvprw-nuclei,
title = {{Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks}},
author = {Ho, David Joon and Fu, Chichen and Salama, Paul and Dunn, Kenneth W. and Delp, Edward J.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2017},
pages = {834-842},
doi = {10.1109/CVPRW.2017.116},
url = {https://mlanthology.org/cvprw/2017/ho2017cvprw-nuclei/}
}