| ---
|
| 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/).
|
|
|
