README.md

metadata

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

IQ-demodulated multi-focal ultrasound channel data from the Stanford Ultrasound RF Channel dataset, aggregated for self-supervised pre-training (MAE / JEPA / contrastive). The signals are not beamformed — each frame retains its per-transducer channel data after sub-aperture combination and IQ demodulation.

Files

File	Description
ultrasonic_dataset.zarr/	6D float32 zarr array, Blosc-LZ4 compressed, stored as an unzipped directory tree (one file per chunk, ~28.6 GB total). One chunk per frame on the leading axis → consumers can fetch just the first N frames.
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: (168, 192, 192, 3, 2, 215)
• Axes: ['frame', 'transducer', 'scanline', 'focal_zone', 'iq', 'sample']
• Chunks: (1, 192, 192, 3, 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; scripts/dataset_scripts/visualize_beamformed_pymust.py beamforms the same channel data for visualisation only (the stored array stays unbeamformed).

Frame counts

By subject:

Subject	Frames
Rat1	12
Rat10	6
Rat11	6
Rat12	6
Rat13	6
Rat14	6
Rat15	6
Rat16	6
Rat18	6
Rat19	6
Rat2	12
Rat20	6
Rat3	12
Rat4	12
Rat5	12
Rat6	12
Rat7	12
Rat8	12
Rat9	6
VerasonicsAcq	6

By subject × subfolder:

Subject	Subfolder	Frames
Rat1	—	6
Rat1	ExposedLiver	6
Rat10	—	6
Rat11	—	6
Rat12	—	6
Rat13	—	6
Rat14	—	6
Rat15	—	6
Rat16	—	6
Rat18	—	6
Rat19	—	6
Rat2	—	6
Rat2	ExposedLiver	6
Rat20	—	6
Rat3	—	6
Rat3	ExposedLiver	6
Rat4	—	6
Rat4	ExposedLiver	6
Rat5	—	6
Rat5	ExposedLiver	6
Rat6	—	6
Rat6	ExposedLiver	6
Rat7	—	6
Rat7	ExposedLiver	6
Rat8	—	6
Rat8	ExposedLiver	6
Rat9	—	6
VerasonicsAcq	Rat1	6

Loading

from src.dataset import build_split_datasets

splits, meta = build_split_datasets(
    "benbarkow/us-ssl-mf",
    split_strategy="file",  # or "subject"
    cache_dir="/tmp/hf_cache",
    max_frames=16,          # optional: fetch only the first 16 frames' chunks
)
train_ds = splits["train"]

max_frames exploits the per-frame chunking: only the chunks for the first N frames are downloaded, so you can iterate on a small box without pulling the whole array. Omit it to fetch everything.

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.