Spheroid Segmentation Using Multiscale Deep Adversarial Networks
Abstract
In this work, we segment spheroids with different sizes, shapes, and illumination conditions from bright-field microscopy images. To segment the spheroids we create a novel multiscale deep adversarial network with different deep feature extraction layers at different scales. We show that linearly increasing the adversarial loss contribution results in a stable segmentation algorithm for our dataset. We qualitatively and quantitatively compare the performance of our deep adversarial network with two other networks without adversarial losses. We show that our deep adversarial network performs better than the other two networks at segmenting the spheroids from our 2D bright-field microscopy images.
Cite
Text
Sadanandan et al. "Spheroid Segmentation Using Multiscale Deep Adversarial Networks." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.11Markdown
[Sadanandan et al. "Spheroid Segmentation Using Multiscale Deep Adversarial Networks." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/sadanandan2017iccvw-spheroid/) doi:10.1109/ICCVW.2017.11BibTeX
@inproceedings{sadanandan2017iccvw-spheroid,
title = {{Spheroid Segmentation Using Multiscale Deep Adversarial Networks}},
author = {Sadanandan, Sajith Kecheril and Karlsson, Johan and Wählby, Carolina},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {36-41},
doi = {10.1109/ICCVW.2017.11},
url = {https://mlanthology.org/iccvw/2017/sadanandan2017iccvw-spheroid/}
}