Feature Augmented Deep Neural Networks for Segmentation of Cells
Abstract
In this work, we use a fully convolutional neural network for microscopy cell image segmentation. Rather than designing the network from scratch, we modify an existing network to suit our dataset. We show that improved cell segmentation can be obtained by augmenting the raw images with specialized feature maps such as eigen value of Hessian and wavelet filtered images, for training our network. We also show modality transfer learning, by training a network on phase contrast images and testing on fluorescent images. Finally we show that our network is able to segment irregularly shaped cells. We evaluate the performance of our methods on three datasets consisting of phase contrast, fluorescent and bright-field images.
Cite
Text
Sadanandan et al. "Feature Augmented Deep Neural Networks for Segmentation of Cells." European Conference on Computer Vision Workshops, 2016. doi:10.1007/978-3-319-46604-0_17Markdown
[Sadanandan et al. "Feature Augmented Deep Neural Networks for Segmentation of Cells." European Conference on Computer Vision Workshops, 2016.](https://mlanthology.org/eccvw/2016/sadanandan2016eccvw-feature/) doi:10.1007/978-3-319-46604-0_17BibTeX
@inproceedings{sadanandan2016eccvw-feature,
title = {{Feature Augmented Deep Neural Networks for Segmentation of Cells}},
author = {Sadanandan, Sajith Kecheril and Ranefall, Petter and Wählby, Carolina},
booktitle = {European Conference on Computer Vision Workshops},
year = {2016},
pages = {231-243},
doi = {10.1007/978-3-319-46604-0_17},
url = {https://mlanthology.org/eccvw/2016/sadanandan2016eccvw-feature/}
}