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Datasets:
ChipYTY
/
PASD

Tasks:
Image Segmentation
Image Classification
Modalities:
3D
Languages:
English
Size:
n<1K
Tags:
medical-imaging
mri
3d
placenta
pas
placenta-accreta-spectrum
License:
Dataset card Files
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  1. README.md +147 -3
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README.md CHANGED
@@ -1,3 +1,147 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: mit
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+ task_categories:
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+ - image-segmentation
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+ - image-classification
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+ language:
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+ - en
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+ tags:
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+ - medical-imaging
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+ - mri
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+ - 3d
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+ - placenta
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+ - pas
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+ - placenta-accreta-spectrum
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+ - obstetrics
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+ size_categories:
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+ - n<1K
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+ pretty_name: PASD - Placenta Accreta Spectrum MRI Dataset
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+ ---
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+
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+ # PASD — Placenta Accreta Spectrum MRI Dataset
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+
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+ A 3D MRI dataset for **Placenta Accreta Spectrum (PAS)** diagnosis with
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+ voxel-level lesion masks and case-level diagnostic labels. This dataset
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+ accompanies the paper:
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+
27
+ > **3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum**, IEEE Transactions on Image Processing.
28
+
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+ Source code for the proposed 3DSAMba method:
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+ [https://github.com/Drchip61/PASD](https://github.com/Drchip61/PASD).
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+
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+ ## Dataset Summary
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+
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+ | Split | Cases | Negative (label=0) | Positive (label=1) |
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+ | ----- | ----- | ------------------ | ------------------ |
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+ | train | 184 | 61 | 123 |
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+ | test | 60 | 20 | 40 |
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+ | total | 244 | 81 | 163 |
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+
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+ Each case contains a single transverse-plane T2-weighted MRI volume of the
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+ uterus and the corresponding binary segmentation mask covering the suspected
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+ lesion region. Volumes are saved as NIfTI files (`.nii.gz`) at their native
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+ resolution; typical shape is `(560, 560, ~55-70)` with `float64` intensities
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+ in roughly `[0, 3500]`.
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+
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+ ## Files & Layout
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+
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+ ```
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+ PASD/
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+ ├── train/
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+ │ ├── PASD_00001_1/
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+ │ │ ├── PASD_00001_1_image.nii.gz # MRI volume
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+ │ │ └── mask.nii.gz # binary segmentation mask
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+ │ ├── PASD_00002_1/
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+ │ │ └── ...
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+ │ └── PASD_00184_1/
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+ └── test/
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+ ├── PASD_00185_1/
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+ │ └── ...
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+ └── PASD_00244_0/
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+ ```
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+
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+ - The directory name encodes the case id and the **case-level class label**
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+ (`PASD_<5-digit-id>_<label>`), where `label ∈ {0, 1}` indicates
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+ PAS-negative or PAS-positive respectively.
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+ - Inside every case directory there is exactly one MRI volume
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+ (`*_image.nii.gz`) and one segmentation mask (`mask.nii.gz`).
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+
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+ This layout is the one expected by the dataloaders in the reference
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+ implementation. The classifier-stage `dataset_class.py` additionally reads
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+ predicted masks from a sibling directory (`test_other/`) — see the
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+ repository for details.
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+
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+ ## Privacy / De-identification
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+
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+ All cases have been **fully de-identified**:
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+
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+ - Original patient-name pinyin and hospital sequence numbers have been
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+ removed from both directory names and file names.
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+ - NIfTI header fields that *could* contain free text (`descrip`,
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+ `intent_name`, `aux_file`, `db_name`) are emptied. They were already empty
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+ in the source data, but we scrub them defensively.
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+ - No DICOM tags, accession numbers, or acquisition timestamps are
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+ distributed with the dataset.
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+
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+ The internal mapping between original case identifiers and the released
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+ `PASD_xxxxx` ids is **not** part of this release and is kept only by the
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+ data custodians.
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+
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+ ## How to Load
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+
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+ ### Plain PyTorch
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+
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+ ```python
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+ import os
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+ import nibabel as nib
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+
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+ CASE_DIR = "PASD/train/PASD_00001_1"
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+
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+ mri = nib.load(os.path.join(CASE_DIR, "PASD_00001_1_image.nii.gz")).get_fdata()
101
+ msk = nib.load(os.path.join(CASE_DIR, "mask.nii.gz")).get_fdata()
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+
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+ label = int(CASE_DIR[-1]) # 0 = PAS-negative, 1 = PAS-positive
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+ print(mri.shape, msk.shape, label)
105
+ ```
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+
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+ ### Hugging Face Datasets
108
+
109
+ ```python
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+ from huggingface_hub import snapshot_download
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+
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+ local_dir = snapshot_download(
113
+ repo_id="ChipYTY/PASD",
114
+ repo_type="dataset",
115
+ )
116
+ ```
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+
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+ After the snapshot is available locally, the `train/` and `test/` folders
119
+ can be plugged directly into the reference implementation's `dataset.py`.
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+
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+ ## Intended Use
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+
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+ - Lesion segmentation on placenta-region MRI.
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+ - PAS positive vs. negative classification.
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+ - Multi-task learning that couples segmentation and diagnosis.
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+
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+ The dataset is intended for research purposes only. It is **not** a
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+ substitute for clinical judgement and should not be used to make individual
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+ diagnoses.
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+
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+ ## Citation
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+
133
+ ```bibtex
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+ @article{zhang2025pasd,
135
+ title = {3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum},
136
+ author = {Zhang, Yuliang and He, Fang and Peng, Lulu and Guo, Qing and Yu, Lin and
137
+ Wang, Zhijian and Shun, Wei and Liu, Jue and Chen, Yonglu and Huang, Jianwei and
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+ Bao, Zeye and Cai, Zhishan and Chen, Yanhong and Hu, Miao and Gu, Zhongjia and
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+ Shi, Yiyu and Yan, Tianyu and Zhang, Pingping and Ting, Song and Du, Lili and Chen, Dunjin},
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+ journal = {IEEE Transactions on Image Processing},
141
+ year = {2025}
142
+ }
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+ ```
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+
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+ ## License
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+
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+ Released under the [MIT License](https://opensource.org/license/mit/).
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