| --- |
| pretty_name: SSL Ultrasound Representation (Stanford RF MultiFocal) |
| license: other |
| task_categories: |
| - feature-extraction |
| tags: |
| - ultrasound |
| - self-supervised |
| - rf-data |
| - mae |
| - jepa |
| size_categories: |
| - n<1K |
| --- |
| |
| # SSL Ultrasound Representation Dataset |
|
|
| Beamformed multi-focal ultrasound RF data from the Stanford Ultrasound RF Channel |
| dataset, aggregated for self-supervised pre-training (MAE / JEPA / contrastive). |
|
|
| ## Files |
|
|
| | File | Description | |
| |------|-------------| |
| | `ultrasonic_dataset.zarr.zip` | 6D float32 zarr array, Blosc-LZ4 compressed (2.4 GB). | |
| | `ultrasonic_dataset.metadata.json` | Per-frame provenance (subject, file, subfolder, indices). | |
| | `README.md` | This card (auto-generated by `push_to_hf.py`). | |
|
|
| ## Array layout |
|
|
| - **Name:** `rf_iq` |
| - **Shape:** `(14, 192, 192, 3, 2, 215)` |
| - **Axes:** `['frame', 'transducer', 'scanline', 'focal_zone', 'iq', 'sample']` |
| - **Chunks:** `(1, 32, 32, 1, 2, 215)` (one frame per chunk on the leading axis — aligned with DataLoader workers). |
| - **Dtype:** `float32` |
| - **IQ index:** `0` = in-phase, `1` = quadrature. |
|
|
| ## Processing pipeline |
|
|
| 1. Source: Verasonics Vantage `.mat` files with `MultiFocal` in the filename |
| (each file holds a 2-frame cine via `Resource.RcvBuffer.numFrames = 2`). |
| 2. Sub-aperture combination + IQ demodulation via `pymust.rf2iq`. |
| 3. Fast-time axis truncated at **860 samples** |
| (everything beyond is noise/zeros), then decimated by |
| **4×** → **215** output samples per signal. |
| 4. Stacked into a single 6D array with chunks optimised for 3D MAE patching. |
|
|
| See `scripts/dataset_scripts/build_mae_dataset.py` in the source repo for the |
| exact pipeline; see `scripts/dataset_scripts/visualize_beamformed_pymust.py` for |
| the reference beamformer. |
|
|
| ## Frame counts |
|
|
| By subject: |
|
|
| | Subject | Frames | |
| |---------|--------| |
| | `rat1` | 8 | |
| | `rat2` | 6 | |
|
|
| By subject × subfolder: |
|
|
| | Subject | Subfolder | Frames | |
| |---------|-----------|--------| |
| | `rat1` | `—` | 6 | |
| | `rat1` | `exposed_liver` | 2 | |
| | `rat2` | `—` | 6 | |
|
|
| ## Loading |
|
|
| ```python |
| from src.dataset import build_split_datasets |
| |
| splits, meta = build_split_datasets( |
| "benbarkow/test-us-ssl", |
| split_strategy="file", # or "subject" |
| cache_dir="/tmp/hf_cache", |
| ) |
| train_ds = splits["train"] |
| ``` |
|
|
| The split helpers (`split_by_subject`, `split_by_file`) avoid cross-split leakage: |
| both frames of the same 2-frame cine always end up in the same split, and (for |
| `split_by_subject`) no subject appears in two splits. |
|
|
