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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 paper

Repository Structure

  • src/ contains executable files used to run the model. Further information on running the code can be found in the corresponding directory.
  • loader/contains scripts for data loading and self implemented augmentation functions.
  • metrics/contains evaluation code.
  • misc/contains util scripts.
  • model/ contains scripts that define the architecture of the segmentation models.
  • opt/ contains scripts that define the model hyperparameters.
  • postproc/ contains post processing utils.
  • config.py is the configuration file. Paths need to be changed accordingly.
  • train.py and infer.py are the training and inference scripts respectively.
  • process.py is the post processing script for obtaining the final instances.
  • extract_patches.py is the patch extraction script.

HoVer-Net

Citation

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

Graham, Simon, Quoc Dang Vu, Shan E. Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, and Nasir Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." arXiv preprint arXiv:1812.06499v4 (2019).

BibTex entry: 

@article{graham2019hover,
  title={HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images},
  author={Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, Nasir Rajpoot},
  journal={arXiv preprint arXiv:1812.06499v4},
  year={2019}
}

Overlaid Segmentation and Classification Prediction

Segmentation

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

Authors

Additional Implementations available

Results

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

Extra

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

License

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