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.

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

License

CC BY 4.0 (LYON19 source images) + Warwick citation-required for NuClick annotations

Redistribution and commercial use are permitted under the terms below.

Source of the terms: https://zenodo.org/records/3385420

Audit note (verbatim from the MedOtter dataset card):

IHC images are from LYON19, licensed CC BY 4.0 on Zenodo (api license.id=cc-by-4.0, open); Warwick NuClick page adds only a citation requirement, no DUA/ND/NC. Redistribution+commercial OK with attribution. triage=3 is wrong. Add CC BY/citation notice; 'other' tag is imprecise.

Please cite:

Swiderska-Chadaj et al., "Learning to detect lymphocytes in immunohistochemistry with deep learning.", Medical Image Analysis 58:101547, 2019. doi:10.1016/j.media.2019.101547
Alemi Koohbanani et al., "NuClick: A deep learning framework for interactive segmentation of microscopic images.", Medical Image Analysis 65:101771, 2020. doi:10.1016/j.media.2020.101771