EchoXFlow - 2-D B-mode subset (zea format)

This is a derived, partial copy of EchoXFlow by Stenhede et al. (Akershus University Hospital). All credit for the data goes to the original authors. Please cite their paper (see Citation) and use the original dataset if you need the full data.

• Paper: EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Function (NeurIPS 2026 Datasets and Benchmarks)
• Original dataset: Ahus-AIM/EchoXFlow
• Code: github.com/Ahus-AIM/EchoXFlow
• License: CC BY-NC-SA 4.0, inherited from the original dataset

EchoXFlow contains 666 routine-care echocardiographic examinations (35,377 recordings) stored in the scanner's native beamspace (polar) grid, with co-registered ECG, B-mode, Doppler and dense/sparse annotations. This repository re-packages only the single-plane 2-D B-mode recordings into the zea HDF5 format, so they can be loaded directly with zea.

What is included

Recordings	13,619 single-plane 2-D B-mode recordings (of 26,356 in EchoXFlow)
Examinations	666 (all)
Selection	frame rate > 30 Hz and >= 10 frames
Per file	B-mode frames (uint8, beamspace / polar grid, not scan-converted), per-pixel Cartesian coordinates, frame timestamps, ECG (when available), exam id
Size	~276 GB

data/
  <exam_id>/
    <recording_id>.hdf5

Limitations and differences from the original

This is not the full EchoXFlow dataset. Compared to the original it is missing:

• All other modalities: M-mode, dual-plane, triplane and 3-D B-mode, colour/tissue Doppler, pulsed- and continuous-wave Doppler.
• Single-plane B-mode recordings with frame rate <= 30 Hz or fewer than 10 frames (e.g. probe-positioning or single-frame captures).
• All annotations (speckle-tracking contours, 3-D LV meshes, sample volumes, measurements, spectral traces, QRS triggers).
• Most recording metadata from the Zarr stores and croissant.json (e.g. derived/ quantities, acquisition parameters beyond sector geometry, SHA-256 checksums).
• splits.csv (patient-level five-fold split) and view_classifier_probabilities.csv (view labels). Use the files from the original repository with the exam_id / recording_id from the file paths.

The conversion was made from the EchoXFlow release available in June 2026; later upstream changes are not reflected here.

The limitations of the original dataset also apply: single-centre, single-vendor data from a selected population. It is not representative of all institutions, vendors, protocols or patient subgroups, and it is not validated for clinical diagnosis, triage, treatment or prognosis.

Usage

import zea

path = "hf://zeahub/echoxflow/data/exam_0068f33be3ce3c27/recording_0110895d4a45b99e.hdf5"
with zea.File(path) as f:
    image = f.tracks[0].data.image
    frames = image.values[:]       # (n_frames, n_depth, n_angles), uint8, beamspace
    coords = image.coordinates[:]  # (n_depth, n_angles, 3), Cartesian (x, y, z) in metres
    ecg = f.metadata.ecg.samples[:]

The files were created with the zea converter (requires the echoxflow package):

zea convert echoxflow <echoxflow_data_root> <dst> --min-frames 10 --min-fps 30

License

CC BY-NC-SA 4.0, the same license as the original EchoXFlow dataset. You may share and adapt the data for non-commercial purposes, provided you give appropriate credit to the EchoXFlow authors, indicate changes, and distribute derivatives under the same license. Changes made here: the subset selection and format conversion described above.

Citation

If you use this data, cite the original EchoXFlow paper:

@inproceedings{stenhede_2026_echoxflow,
  title = {EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Function},
  author = {Stenhede, Elias and Sulkowska, Joanna and Orstad, Eivind Bj{\o}rkan and Schirmer, Henrik and Ranjbar, Arian},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track},
  year = {2026},
  eprint = {2605.05447},
  doi = {10.48550/arXiv.2605.05447},
  url = {https://arxiv.org/abs/2605.05447}
}