---
license: cc-by-4.0
task_categories:
- image-to-image
- image-segmentation
language: en
tags:
- pathology
- breast-cancer
- virtual-staining
- ihc
- h-and-e
- whole-slide-image
- acrobat
- computational-pathology
size_categories:
- 10M<n<100M
pretty_name: ACROBAT Registered H&E-IHC Patches v3
---

# ACROBAT Registered H&E-IHC Patches v3

H&E-IHC patch pairs extracted from registered ACROBAT whole-slide images. All IHC slides were warped into H&E space by VALIS — patches at the same (x, y) coordinates in HE and IHC are perfectly aligned.

## Key Features

- **205 patients** — full ACROBAT breast cancer dataset
- **9,658 HE patches** at 1024×1024 px, 0.92 µm/px (10X)
- **31K IHC pairs**: ER (8,385), PGR (8,453), HER2 (5,543), KI67 (8,439)
- **WSI thumbnails** — 512×512 low-res H&E per patient for region-context training
- **WSI dimensions** — per-patient for coordinate normalization
- **VALID registration** — all IHC slides warped into H&E space
- **Filtered** — edge-overlap verification, cross-stain consistency, cellularity ranking
- **HDF5 format** with LZF compression

## Quick Facts

| Property | Value |
|---|---|
| File | `patches_v3.h5` |
| Size | 105 GB |
| Format | HDF5, LZF compression, uint8 |
| Patients | 205 |
| Patch size | 1024 × 1024 px |
| Resolution | 0.92 µm/px (10X) |
| Stride | 1024 px (non-overlapping) |

## HDF5 Schema

```
patches_v3.h5
├── he                    uint8  (9658, 1024, 1024, 3)
├── er                    uint8  (8385, 1024, 1024, 3)
├── pgr                   uint8  (8453, 1024, 1024, 3)
├── her2                  uint8  (5543, 1024, 1024, 3)
├── ki67                  uint8  (8439, 1024, 1024, 3)
├── index/
│   ├── patient_id        int32  (9658,)          patient ID for each HE patch
│   ├── coord_x           int32  (9658,)          H&E level-0 x coordinate
│   ├── coord_y           int32  (9658,)          H&E level-0 y coordinate
│   ├── pair_er           int32  (8385, 2)        [he_idx, er_idx] pairs
│   ├── pair_pgr          int32  (8453, 2)
│   ├── pair_her2         int32  (5543, 2)
│   ├── pair_ki67         int32  (8439, 2)
│   ├── edge_overlap_er   float32 (8385,)
│   ├── edge_overlap_pgr  float32 (8453,)
│   ├── edge_overlap_her2 float32 (5543,)
│   └── edge_overlap_ki67 float32 (8439,)
├── patient_{id}/          (v3) per-patient group
│   ├── thumbnail          uint8  (512, 512, 3)   low-res H&E WSI thumbnail
│   └── .attrs: wsi_width, wsi_height
└── metadata/
    └── created_at         string
```

### Sparse Pairs

Pair arrays are sparse. Not every HE position has an IHC counterpart for every stain. Use `pair_{stain}[i, 0]` for the HE index and `pair_{stain}[i, 1]` for the IHC index.

## Usage

```python
import h5py
import torch
import numpy as np

with h5py.File("patches_v3.h5", "r") as f:
    # Load ER pairs
    pairs_er = f["index/pair_er"][:]
    he_idx, er_idx = pairs_er[0]

    # Get patches
    he_patch = f["he"][he_idx]        # (1024, 1024, 3) uint8
    er_patch = f["er"][er_idx]        # (1024, 1024, 3) uint8

    # Patient info
    pid = int(f["index/patient_id"][he_idx])
    x, y = f["index/coord_x"][he_idx], f["index/coord_y"][he_idx]

    # Thumbnail (v3)
    thumb = f[f"patient_{pid}/thumbnail"][:]  # (512, 512, 3)
    wsi_w = f[f"patient_{pid}"].attrs["wsi_width"]
    wsi_h = f[f"patient_{pid}"].attrs["wsi_height"]
```

## Content Filtering

| Filter | Threshold | Removes |
|---|---|---|
| HE content | std >= 15, tissue >= 0.05 | Adipose, blank glass |
| IHC content | std >= 8 | Blank IHC patches |
| Edge-overlap Dice | >= 0.08 | Misregistered IHC |
| Cross-stain consistency | Majority rule | Single-stain failures |
| Per-patient cap | 150 top by cellularity | Redundant patches |
| Grid stride | 1024 px | Non-overlapping grid |

## Citation

If you use this dataset, please cite the ACROBAT challenge and UNIStainNet:

```bibtex
@inproceedings{weitz2023acrobat,
  title={ACROBAT -- a multi-stain breast cancer dataset for virtual staining},
  author={Weitz, Philippe and others},
  booktitle={MICCAI},
  year={2023}
}
```