zhangpeihao / Triple_U-net

The code of Triple U-net: Hematoxylin-aware Nuclei Segmentation with Progressive Dense Feature Aggregation.

Triple U-net: Hematoxylin-aware Nuclei Segmentation with Progressive Dense Feature Aggregation

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

Prerequisites

• python==3.6
• torch==1.3.0
• opencv-python==4.1.2.30
• scikit-image
• matplotlib
• numpy

Dataset

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

Code

Check the "source/README.md" for more details.

Download the Model

You can download our trained model via the following Google Drive link.

Google Drive

Baidu Pin: yaib

Citation

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}
}