---
license: other
license_name: warwick-tia-citation-required
task_categories:
  - image-segmentation
tags:
  - medical
  - histopathology
  - ihc
  - immunohistochemistry
  - lymphocytes
  - nuclei
  - nuclick
pretty_name: NuClick-IHC
---

# NuClick-IHC (Lymphocyte Segmentation in IHC)

Immunohistochemistry (IHC) stained histopathology patches of lymphocytes with
per-nucleus instance segmentation masks. Released by the Warwick TIA Centre as
the IHC component of the NuClick framework's training/validation data, with
ROIs sourced from the LYON19 cohort (CD3/CD8 IHC of breast, colon, prostate).

## Overview
- **Modality:** Histopathology (IHC, RGB microscopy)
- **Tissue:** Lymphocytes in CD3/CD8-stained breast/colon/prostate
- **Image size:** 256x256 RGB
- **Samples:** 671 train + 200 validation = 871
- **Ground truth:** Per-nucleus instance segmentation masks generated by the
  NuClick interactive tool and refined for training. The paper validates these
  by showing a model trained on them placed first on LYON19.

## Columns
| Column | Type | Notes |
|---|---|---|
| `id` | string | ROI identifier (e.g. `ROI_100_1`) |
| `image` | Image (RGB) | 256x256 IHC patch |
| `mask` | Image (mode `L`) | 256x256 uint8 instance map: 0 = background, 1..N = instance IDs |
| `num_nuclei` | int32 | Number of nuclei instances in the patch (0 if empty) |

## Notes
- Approximately 30% of training patches and 25% of validation patches contain
  no nuclei (`num_nuclei == 0`, mask is all-zero). This matches the source
  release.
- Max instances per patch in this release is 69, so a uint8 mask losslessly
  preserves all instance IDs.
- For semantic (foreground/background) use, threshold the mask with `mask > 0`.

## Derivation
Source: `ihc_nuclick.zip` from https://warwick.ac.uk/fac/cross_fac/tia/data/nuclick/
(IHC subset). The source ships 256x256 PNG images and uint32 .npy instance
maps; we re-encode masks as uint8 PNG (lossless under the observed instance
count). The companion `IHC_xml_asap/` folder contains the raw ASAP-compatible
polygon annotations and is not included here.

## License
The Warwick TIA release does not provide an explicit dataset license. Users
must cite the NuClick paper when publishing work derived from it.

## Citation
- Alemi Koohbanani N., Jahanifar M., Zamani Tajadin N., Rajpoot N. *NuClick: A
  deep learning framework for interactive segmentation of microscopic images.*
  Medical Image Analysis, 65:101771, 2020. doi:10.1016/j.media.2020.101771