To obtain instance-masks from the trained model on the MoNuSAC test data in the required challenge format:
- Ensure library requirements mentioned in requirements.txt are installed
- Download
MoNuSAC test-data
& trained model - Modify
data_dir
,output-dir
,model-path
to reflect MoNuSAC test data, desired output path, & downloaded trained model path (.index file) respectively.
Run the below script as:
python test_script.py --data_dir --output_dir --model_path --img_ext --gpu
Install the required libraries before using this code. Please refer to requirements.txt
Download the datasets & modify the paths in config_multitask
:
Ground truth files are in .mat
format, refer to the README included with the datasets for further information.
Files needed to modify multi-task behavior:
- test_script : Test & Eval script
- config_multitask : Config
- train_multitask : Training Script
- hover_multitask : Network & Training protocol
For details on the general repository structure, refer to hover_net-README.md
MoNuSAC 2020
@article{monusac2020,
author = {Verma, Ruchika; Kumar, Neeraj; Patil, Abhijeet; Kurian, Nikhil; Rane, Swapnil; and Sethi, Amit},
year = {2020},
month = {02},
pages = {},
language = {en},
title = {Multi-organ Nuclei Segmentation and Classification Challenge 2020},
publisher = {Unpublished},
doi = {10.13140/RG.2.2.12290.02244/1},
url = {http://rgdoi.net/10.13140/RG.2.2.12290.02244/1}
}
HoVer-Net Paper [Linked here]
@article{graham2019hover,
title={Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images},
author={Graham, Simon and Vu, Quoc Dang and Raza, Shan E Ahmed and Azam, Ayesha and Tsang, Yee Wah and Kwak, Jin Tae and Rajpoot, Nasir},
journal={Medical Image Analysis},
pages={101563},
year={2019},
publisher={Elsevier}
}