aiSEGcell
Overview
aiSEGcell
- convolutional neural network-based software to segment nuclei and cells in bright field images
- only brightfield images are sufficient - no fluorescent labeling of nuclei required
- with current training, it segments nuclei and whole cells robustly across different cell types. It is easily adaptable to new experimental settings, new cell types, new organelles, etc. by retraining with very little new image data
- user-friendly, no experience with coding or neural networks required
- both a command line interface and a napari plugin (graphical user interface) are available
See Instructions (below) for more information.
Instructions
Instructions on how to install and use the software can be found on the respective GitHub repositories of external page aiSEGcell and external page napari-aiSEGcell.
Download aiSEGcell
Published (Schirmacher, PLOS Computational Biology 2024) versions of aiSEGcell and napari-aiSEGcell are available at a ETH Research Collection repository: external page https://doi.org/10.3929/ethz-b-000679085
Actively maintained code repositories are available on GitHub:
- aiSEGcell: external page https://github.com/CSDGroup/aisegcell
- napari-aiSEGcell: external page https://github.com/CSDGroup/napari-aisegcell
Datasets
All data sets to train and evaluate aiSEGcell are available at a ETH Research Collection repository: external page https://doi.org/10.3929/ethz-b-000679085
References
If you use aiSEGcell, please cite the following publication:
Schirmacher*, Armagan, Zhang, Kull, Auler, Schroeder*
external page aiSEGcell: user-friendly deep learning-based segmentation of nuclei in transmitted light images
PLOS Computational Biology, 20(8):e1012361
License
BSD 3-Clause License
Copyright (c) 2022, Schroeder Lab
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.