Triple U-net: Hematoxylin-aware Nuclei Segmentation with Progressive Dense Feature Aggregation, Bingchao Zhao, Xin Chen, Zhi Li, Zhiwen Yu, Su Yao, Lixu Yan, Yuqian Wang, Zaiyi Liu, Changhong Liang and Chu Han, Medical Image Analysis, 2020. Paper link
- python==3.6
- torch==1.3.0
- opencv-python==4.1.2.30
- scikit-image
- matplotlib
- numpy
The CoNSeP dataset is provided by: Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
The MoNuSeg dataset is provided by: A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
The CPM-17 dataset is provided by: Methods for segmentation and classification of digital microscopy tissue images
Check the "source/README.md" for more details.
You can download our trained model via the following Google Drive link.
Baidu Pin: yaib
If any part of this code is used, please give appropriate citation to our paper.
BibTex entry:
@article{zhao2020triple,
title={Triple U-net: Hematoxylin-aware Nuclei Segmentation with Progressive Dense Feature Aggregation},
author={Zhao, Bingchao and Chen, Xin and Li, Zhi and Yu, Zhiwen and Yao, Su and Yan, Lixu and Wang, Yuqian and Liu, Zaiyi and Liang, Changhong and Han, Chu},
journal={Medical Image Analysis},
pages={101786},
year={2020},
publisher={Elsevier}
}