---
license: cc-by-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - histopathology
  - immunohistochemistry
  - ihc
  - multiplex-immunofluorescence
  - ki67
  - cell-segmentation
  - deepliif
pretty_name: DeepLIIF
size_categories:
  - 1K<n<10K
---

# DeepLIIF (Deep Learning-Inferred Immunofluorescence)

Co-registered IHC (Ki67-DAB brightfield) and multiplex immunofluorescence (mpIF)
patches with cell-level segmentation + classification ground truth for
quantification of clinical pathology slides.

Source paper: Ghahremani et al., "Deep learning-inferred multiplex
immunofluorescence for immunohistochemical image quantification,"
*Nature Machine Intelligence* 4(4):401-412, 2022.
DOI: [10.1038/s42256-022-00471-x](https://doi.org/10.1038/s42256-022-00471-x).
Zenodo: <https://zenodo.org/records/4751737>.

## Overview
- **Modality:** Histopathology - IHC brightfield + co-registered mpIF (DAPI, Lap2, Ki67 marker)
- **Patch size:** 512x512 each (originals are 3072x512 PNGs concatenating six panels)
- **Tissue:** Bladder + Lung (main DeepLIIF), Breast carcinoma (BC-DeepLIIF subset)
- **Total samples:** 1,715
  - `train` (DeepLIIF): 575
  - `validation` (DeepLIIF): 91
  - `test` (DeepLIIF): 598
  - `bc_train` (BC-DeepLIIF, breast): 385
  - `bc_validation` (BC-DeepLIIF, breast): 66

## Columns
| Column | Type | Notes |
|---|---|---|
| `sample_id` | string | Original PNG stem |
| `tissue` | ClassLabel(3) | `0=BC`, `1=Bladder`, `2=Lung` |
| `subset` | string | `DeepLIIF` (main) or `BC-DeepLIIF` |
| `ihc` | Image (RGB) | Input - 512x512 brightfield Ki67-DAB IHC |
| `hematoxylin` | Image (RGB) | Aux target - reconstructed hematoxylin channel |
| `dapi` | Image (RGB) | Aux target - mpIF DAPI nuclear stain |
| `lap2` | Image (RGB) | Aux target - mpIF Lap2 nuclear-envelope stain |
| `marker` | Image (RGB) | Aux target - mpIF Ki67 marker channel |
| `seg_mask` | Image (RGB) | **Ground truth** - red=Ki67+ cell, blue=Ki67- cell, green=boundary, black=background |

## Ground Truth
The `seg_mask` column is the canonical GT. It was generated by combining
per-modality instance segmentations from mpIF DAPI + Lap2 + Hematoxylin + IHC,
with instance boundaries initialized by the ImPartial interactive framework on
DAPI. Cells are then classified red/blue based on Ki67 marker positivity.

For binary semantic segmentation (nucleus vs background) treat (red OR blue)
pixels as foreground. For positive-vs-negative classification, decode red and
blue channels separately.

## Derivation
Each source PNG was sliced column-wise into six 512x512 panels:
- Columns [0:512]       -> `ihc`
- Columns [512:1024]    -> `hematoxylin`
- Columns [1024:1536]   -> `dapi`
- Columns [1536:2048]   -> `lap2`
- Columns [2048:2560]   -> `marker`
- Columns [2560:3072]   -> `seg_mask`

No other preprocessing.

## License
CC BY 4.0 (dataset, per Zenodo record 4751737). The DeepLIIF code repo is
Apache 2.0 with Commons Clause; that license applies only to code/models, not
to this imaging data.

## Citation
```bibtex
@article{ghahremani2022deep,
  title={Deep learning-inferred multiplex immunofluorescence for
         immunohistochemical image quantification},
  author={Ghahremani, Parmida and Li, Yanyun and Kaufman, Arie and
          Vanguri, Rami and Greenwald, Noah and Angelo, Michael and
          Hollmann, Travis J and Nadeem, Saad},
  journal={Nature Machine Intelligence},
  volume={4}, number={4}, pages={401--412}, year={2022},
  doi={10.1038/s42256-022-00471-x}
}
```