CardiacUDC — apical four-chamber echocardiography video segmentation

2D transthoracic echocardiography from two hospitals ("Site G" and "Site R"), annotated for the four cardiac chambers. Introduced as CardiacUDA in GraphEcho (ICCV 2023); the Kaggle release spells it cardiacUDC. Converted from the official Kaggle release — see Provenance.

This is a video dataset: 364 recordings, 38,619 frames, one row per frame.

Configs

Config	Rows	Contents	Mask
annotated (default)	2,257	every frame that carries ground truth	always present
full_video	38,619	every frame of all 364 videos	present on 2,257, null elsewhere

annotated is exactly full_video[has_mask == True] — same schema, same rows. Use annotated for scoring; use full_video for temporal propagation, ordering by frame_index and grouping by video_id.

Classes

Value	Structure
0	background
1	LV — left ventricle
2	LA — left atrium
3	RA — right atrium
4	RV — right ventricle

Value 2 is the left atrium, not the right ventricle; RV is 4. The numbering walks the heart in a loop. This matches the authors' own loader (datasets/cardiac_uda.py, view '4' branch). Figure 2 of the paper labels the left atrium "(RA)" and lists a fifth structure, "epicardium of left ventricle", which does not exist in this release.

Composition

Seven source folders. No train/val/test directories ship.

Folder	Videos	With GT	Site
Site_G_100	97	97	G
Site_G_29	29	29	G
Site_G_20	21	21	G
Site_R_126	85	84	R
Site_R_52	52	52	R
Site_R_73	70	0	R
label_all_frame	10	10	mixed
Total	364	293

Folder-name numbers do not match their contents (Site_G_100 holds 97, Site_R_126 holds 85). Frame counts range 41–284 (mean 106). Three resolutions ship — 800×600 (335 videos), 1024×768 (24), 640×480 (5) — not the two the source description states.

Two annotation regimes

• Site_* folders — sparse. 5–8 frames annotated per video (mean 5.9), out of ~106. This is the paper's training annotation budget.
• label_all_frame — dense. Every frame annotated, 548 frames over 10 videos. The paper's val/test analogue.

gt_dense distinguishes them; gt_source records which pipeline produced each mask.

⚠️ label_all_frame overlaps Site_R_126 — do not split them apart

Eight of the ten label_all_frame videos are the same recordings as videos in Site_R_126: same subject id, identical frame count, ~88% bit-identical pixels (mean absolute difference ≈4.5/255, correlation 0.70–0.94). They are a dense re-annotation of footage that also ships sparsely annotated, not new material. The remaining two have no Site_* counterpart.

Putting label_all_frame in a test split and Site_* in train therefore leaks 8 of 10 test videos into training. group_id ties each twin pair together — split on group_id, never on video_id or subject.

Splits

A single train split ships. The paper's 8:1:1 division is not distributed, and the near-duplication above makes any naive split leak. Downstream consumers should construct their own split, grouping on group_id.

Subject ids are not unique

85 subject names occur in more than one folder, and those collisions are different recordings (0 of 85 are byte-identical). video_id is {folder}/{subject} and is the only safe key. Grouping on bare subject will silently merge unrelated patients.

What this release changes from source

Every modification below is deliberate and reversible from the original Kaggle archive; nothing else is altered.

1. Contour masks filled. label_all_frame ships hollow ~1px contour outlines, whereas Site_* ships filled regions. Scoring a raw outline measures the rim, not the chamber. Outlines are filled per class with scipy.ndimage.binary_fill_holes (with a 1–3 iteration binary_closing fallback for open contours). Validated: post-fill area/bbox ratio is 0.689, against 0.68 for the natively-filled Site_* masks. Two class instances out of ~2,150 could not be closed and were dropped rather than shipped as rings. mask_filled_from_contour flags affected rows.

   The authors' contour_to_mask was not used: it selects classes by rank within set(unique_values), so on a volume with a missing class it silently renumbers the remaining ones.

2. Stray label values remapped. Two label_all_frame volumes encode two of the chambers as 5 and 6 instead of 1 and 4. Verified spatially against the eight consistent volumes — the stray values sit at the class centroids of 1 and 4 (normalised distance 0.013–0.078, every value matching exactly one class) — and remapped {5→1, 6→4}. label_remapped flags affected rows.

3. One truncated source file salvaged. One Site_R_126 label volume is a truncated gzip in the source archive — the enclosing zip's CRC verifies, so this is not a transfer defect. Its 42 intact frames (7 annotated) are kept; the remaining 21 frames carry no ground truth. video_truncated flags the video.

4. Orientation normalised. Source arrays are (W, H, T); each frame is transposed and rotated 180° into the orientation in which the scanner's on-screen text reads correctly. Applied identically to images and masks.

5. De-identification band. The top max(45, ceil(0.075 × H)) rows of every frame of every video are zeroed — 45 rows at 480px and 600px, 58 at 768px. Some source videos retained scanner header text containing patient identifiers; the band is applied uniformly to all 364 videos rather than selectively, so the redaction does not itself indicate which files were affected. This is a MedOtter modification — upstream is unmodified in this respect.

   The band costs no annotation. Over all 294 label volumes / 2,257 annotated frames at native resolution, the topmost mask row is 81 (480px), 83 (600px) and 144 (768px), against a maximum blank of 58. The build re-checks this per frame and refuses to write a row whose mask intersects the band. deid_blanked_rows records the band height applied to each row.

Known limitations of the source release

• Only the A4C view ships. The paper describes 992 videos across four views (LVLA, PALA, LVSA, A4C); the public release is view 4 only, 364 videos. The other three views and the pulmonary-artery class were announced for late 2024 and have not appeared — Kaggle remains at version 1 (2023-10-30).
• 70 videos in Site_R_73 have no labels at all, and one Site_R_126 label volume is present but entirely empty. Both are shipped in full_video as image-only rows and excluded from annotated.
• Third-party catalogues describing this dataset as "5 structures (LV, RV, MYO, LA, RA)" or "29,283 slices" do not match the public release.

Schema

Column	Type	Notes
image	Image	PNG, grayscale, de-identification band applied
mask	Image	PNG, values 0–4; null where no GT
video_id	string	{folder}/{subject} — the only unique key
folder, site, subject	string	site is G, R, or unknown
view	string	A4C throughout
frame_index, n_frames	int32	order within the recording
height, width	int32	native resolution
has_mask, gt_dense	bool	GT presence / dense-annotation regime
gt_source	string	site_sparse, label_all_frame_filled, none
mask_filled_from_contour	bool	mask was reconstructed from an outline
label_remapped	bool	{5→1, 6→4} applied
video_truncated	bool	source label volume was truncated
group_id	string	split on this — ties near-duplicate videos
deid_blanked_rows	int32	height of the zeroed header band
split	string	train (single split)

Provenance

Retrieved from Kaggle xiaoweixumedicalai/cardiacudc-dataset (version 1, 2023-10-30), 7-part split zip, 4,515,920,293 bytes, extracting to 658 NIfTI files / 4,547,904,911 bytes. Archive CRC verified before conversion.

Licence and attribution

Released under the Apache License, Version 2.0, as declared by the dataset authors on the source Kaggle record. (The GraphEcho code repository is MIT-licensed — a separate grant covering the code, not this data.)

This dataset is a re-hosted and reprocessed copy of "cardiacUDC_dataset"
(a.k.a. CardiacUDA), created by Xiaowei Xu et al., originally released at
https://www.kaggle.com/datasets/xiaoweixumedicalai/cardiacudc-dataset

Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, the dataset is
distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND.

Modifications by MedOtter, per Apache-2.0 section 4(b): conversion from NIfTI to
per-frame PNG in parquet; contour masks polygon-filled; two volumes' stray label
values remapped; one truncated source volume partially salvaged; frame
orientation normalised; a fixed-height header band zeroed on every frame.
See "What this release changes from source" above.

Please cite:

@inproceedings{yang2023graphecho,
  title     = {GraphEcho: Graph-Driven Unsupervised Domain Adaptation for
               Echocardiogram Video Segmentation},
  author    = {Yang, Jiewen and Ding, Xinpeng and Zheng, Ziyang and
               Xu, Xiaowei and Li, Xiaomeng},
  booktitle = {Proceedings of the IEEE/CVF International Conference on
               Computer Vision (ICCV)},
  pages     = {11878--11887},
  year      = {2023}
}