Pipeline for deep-learning based 2D image segmentation of plant root grown in EcoFABs using a Residual U-net.
- License: MIT license
- Documentation: https://rhizonet.readthedocs.io
- Paper: https://www.nature.com/articles/s41598-024-63497-8
pip install rhizonet
- Create patches
patchify_rhizonet --config_file ./setup_files/setup-pprepare.json
- Train
train_rhizonet --config_file ./setup_files/setup-unet2d.json --gpus 2 --strategy ddp --accelerator gpu
- Inference
predict_rhizonet --config_file ./setup_files/setup-predict.json
- Post-processing
postprocess_rhizonet --config_file ./setup_files/setup-processing.json
- Evaluate metrics
evalmetrics_rhizonet ---pred_path "path" --label_path "path" --log_dir "path" --task "binary" --num_classes "2"
This code gives the tools to pre-process 2D RGB images and train a deep learning segmentation model using pytorch-lightning for code organization, logging and metrics for training and prediction. It uses as well the library monai for data augmentation and creating a Residual U-net model. The training patches can be created using the data preparation code for cropping and patching.
The training was done on a dataset of multiple ecofabs (plants with different nutrition types) at the two last timestamps. The use of at least one gpu is necessary for training on small patch-size images. The predictions can be done on any other timestamp by loading the appropriate model path. The Google Colab tutorial below details the steps to do so with a given subset of images and 3 possible model weights (varying with the size of the used patches). It is also possible to apply the post-processing using the Google Colab tutorial on the predicted images which uses cropping and morphological operations, and plot the extracted biomass from the processed predictions.
This Google Colab Tutorial is a short notebook that can load 3 possible model weights depending the model type preferred (3 model weights for each patch size trained model), generate predictions and process these predictions given 2 random unseen EcoFAB images of the same experiment. It also generates plots of the extracted biomass for each nutrition type at each date and compares it to the groundtruth (which is the manually scaled biomass by biologists).
MIT License
Copyright (c) 2025, Zineb Sordo
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Please reference this work:
@article{Sordo2024-ul,
title = "{RhizoNet} segments plant roots to assess biomass and growth for
enabling self-driving labs",
author = "Sordo, Zineb and Andeer, Peter and Sethian, James and Northen,
Trent and Ushizima, Daniela",
abstract = "Flatbed scanners are commonly used for root analysis, but typical
manual segmentation methods are time-consuming and prone to
errors, especially in large-scale, multi-plant studies.
Furthermore, the complex nature of root structures combined with
noisy backgrounds in images complicates automated analysis.
Addressing these challenges, this article introduces RhizoNet, a
deep learning-based workflow to semantically segment plant root
scans. Utilizing a sophisticated Residual U-Net architecture,
RhizoNet enhances prediction accuracy and employs a convex hull
operation for delineation of the primary root component. Its main
objective is to accurately segment root biomass and monitor its
growth over time. RhizoNet processes color scans of plants grown
in a hydroponic system known as EcoFAB, subjected to specific
nutritional treatments. The root detection model using RhizoNet
demonstrates strong generalization in the validation tests of all
experiments despite variable treatments. The main contributions
are the standardization of root segmentation and phenotyping,
systematic and accelerated analysis of thousands of images,
significantly aiding in the precise assessment of root growth
dynamics under varying plant conditions, and offering a path
toward self-driving labs.",
journal = "Scientific Reports",
volume = 14,
number = 1,
pages = "12907",
month = jun,
year = 2024
}
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.