| --- |
| 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} |
| } |
| ``` |
|
|
