---
license: mit
task_categories:
  - image-segmentation
  - image-classification
language:
  - en
tags:
  - medical-imaging
  - mri
  - 3d
  - placenta
  - pas
  - placenta-accreta-spectrum
  - obstetrics
size_categories:
  - n<1K
pretty_name: PASD - Placenta Accreta Spectrum MRI Dataset
---

# PASD — Placenta Accreta Spectrum MRI Dataset

A 3D MRI dataset for **Placenta Accreta Spectrum (PAS)** diagnosis with
voxel-level lesion masks and case-level diagnostic labels. This dataset
accompanies the paper:

> **3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum**, IEEE Transactions on Image Processing.

Source code for the proposed 3DSAMba method:
[https://github.com/Drchip61/PASD](https://github.com/Drchip61/PASD).

## Dataset Summary

| Split | Cases | Negative (label=0) | Positive (label=1) |
| ----- | ----- | ------------------ | ------------------ |
| train | 184   | 61                 | 123                |
| test  | 60    | 20                 | 40                 |
| total | 244   | 81                 | 163                |

Each case contains a single transverse-plane T2-weighted MRI volume of the
uterus and the corresponding binary segmentation mask covering the suspected
lesion region. Volumes are saved as NIfTI files (`.nii.gz`) at their native
resolution; typical shape is `(560, 560, ~55-70)` with `float64` intensities
in roughly `[0, 3500]`.

## Files & Layout

```
PASD/
├── train/
│   ├── PASD_00001_1/
│   │   ├── PASD_00001_1_image.nii.gz   # MRI volume
│   │   └── mask.nii.gz                 # binary segmentation mask
│   ├── PASD_00002_1/
│   │   └── ...
│   └── PASD_00184_1/
└── test/
    ├── PASD_00185_1/
    │   └── ...
    └── PASD_00244_0/
```

- The directory name encodes the case id and the **case-level class label**
  (`PASD_<5-digit-id>_<label>`), where `label ∈ {0, 1}` indicates
  PAS-negative or PAS-positive respectively.
- Inside every case directory there is exactly one MRI volume
  (`*_image.nii.gz`) and one segmentation mask (`mask.nii.gz`).

This layout is the one expected by the dataloaders in the reference
implementation. The classifier-stage `dataset_class.py` additionally reads
predicted masks from a sibling directory (`test_other/`) — see the
repository for details.

## Privacy / De-identification

All cases have been **fully de-identified**:

- Original patient-name pinyin and hospital sequence numbers have been
  removed from both directory names and file names.
- NIfTI header fields that *could* contain free text (`descrip`,
  `intent_name`, `aux_file`, `db_name`) are emptied. They were already empty
  in the source data, but we scrub them defensively.
- No DICOM tags, accession numbers, or acquisition timestamps are
  distributed with the dataset.

The internal mapping between original case identifiers and the released
`PASD_xxxxx` ids is **not** part of this release and is kept only by the
data custodians.

## How to Load

### Plain PyTorch

```python
import os
import nibabel as nib

CASE_DIR = "PASD/train/PASD_00001_1"

mri = nib.load(os.path.join(CASE_DIR, "PASD_00001_1_image.nii.gz")).get_fdata()
msk = nib.load(os.path.join(CASE_DIR, "mask.nii.gz")).get_fdata()

label = int(CASE_DIR[-1])    # 0 = PAS-negative, 1 = PAS-positive
print(mri.shape, msk.shape, label)
```

### Hugging Face Datasets

```python
from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="ChipYTY/PASD",
    repo_type="dataset",
)
```

After the snapshot is available locally, the `train/` and `test/` folders
can be plugged directly into the reference implementation's `dataset.py`.

## Intended Use

- Lesion segmentation on placenta-region MRI.
- PAS positive vs. negative classification.
- Multi-task learning that couples segmentation and diagnosis.

The dataset is intended for research purposes only. It is **not** a
substitute for clinical judgement and should not be used to make individual
diagnoses.

## Citation

```bibtex
@article{zhang2025pasd,
  title   = {3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum},
  author  = {Zhang, Yuliang and He, Fang and Peng, Lulu and Guo, Qing and Yu, Lin and
             Wang, Zhijian and Shun, Wei and Liu, Jue and Chen, Yonglu and Huang, Jianwei and
             Bao, Zeye and Cai, Zhishan and Chen, Yanhong and Hu, Miao and Gu, Zhongjia and
             Shi, Yiyu and Yan, Tianyu and Zhang, Pingping and Ting, Song and Du, Lili and Chen, Dunjin},
  journal = {IEEE Transactions on Image Processing},
  year    = {2025}
}
```

## License

Released under the [MIT License](https://opensource.org/license/mit/).