HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images

A multiple branch network that performs nuclear instance segmentation and classification within a single network. The network leverages the horizontal and vertical distances of nuclear pixels to their centres of mass to separate clustered cells. A dedicated up-sampling branch is used to classify the nuclear type for each segmented instance.

This is an extended version of our previous work: XY-Net.

Link to Medical Image Analysis paper.

NEWS: We have now released an inference version of HoVer-Net with WSI-processing capability trained on ~200,000 nuclei. For further information, click here.

Set Up Environment

conda create --name hovernet python=3.6
conda activate hovernet
pip install -r requirements.txt


Download the CoNSeP dataset as used in our paper from this link.
Download the Kumar, CPM-15, CPM-17 and TNBC datsets from this link.

Ground truth files are in .mat format, refer to the README included with the datasets for further information.

Repository Structure



If any part of this code is used, please give appropriate citation to our paper.

BibTex entry:

  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},

Overlaid Segmentation and Classification Prediction


The colour of the nuclear boundary denotes the type of nucleus.
Blue: epithelial
Red: inflammatory
Green: spindle-shaped
Cyan: miscellaneous


Additional Implementations available

Getting Started

Install the required libraries before using this code. Please refer to requirements.txt


All comparative results on the CoNSeP, Kumar and CPM-17 datasets can be found here.


The cell profiler pipeline that we used in our comparative experiments can be found here.

Companion Sites

The same version of this repository is officially available on the following sites for collection/affiliation purpose


This project is licensed under the MIT License - see the LICENSE file for details