CystoFold: A Sparse Polygon Dataset for Bladder Tissue Fold Prediction

Annotators were instructed to label only regions where the tissue state (Stable, Pre-Fold, or Folded) could be identified with absolute certainty. This "safe-labeling" approach prevents ambiguous tissue from being misclassified. Semantic Classes Stable: Tissue that remains flat and visible throughout the emptying cycle Pre-Fold: Tissue appearing flat in the current frame that will fold before the cycle ends Folded: Tissue currently forming a visible fold Cycle Markers Each video includes explicit markers for the bladder filling cycle.

The marker filled indicates the frame at which the bladder reached a fully filled state; the marker emptied indicates the frame at which it was fully emptied. These markers are provided in the _cycles.csv files and allow temporal alignment of annotations with the physiological cycle phase. The per-frame filling context (Filling or Emptying) is additionally stored in the context field of each annotation record in the JSON and CSV label files.

Dataset Structure patient_1/ (2 filling cycles, 586 usable frames) <video>.mp4 (Original unlabeled video) <video>.csv (Per-polygon annotation records) <video>_cycles.csv (Cycle phase marker timestamps) <video>.json (Full labeler session, restorable) patient_2/ (1 filling cycle, 103 usable frames) ... patient_3/ (1 filling cycle, 184 usable frames) ... Usage Each annotation record in the .csv file corresponds to a single labeled region in one frame.

The relevant columns are: frame — video frame index (0-based) label — class name: Stable (1), Pre-Fold (2), Folded (3); Background is implicitly 0 shape — annotation geometry: polygon (precise boundary, polygon_points field contains a JSON array of [x, y] pairs) or rect (bounding box, use x1, y1, x2, y2 columns) use_frame — set to False for frames that should be excluded; filter to use_frame != False before training frame_quality — exclude frames marked motion_blur context — Filling or Emptying, indicating the bladder phase at that frame To render a segmentation mask for a given frame, iterate over all rows matching that frame index, draw each polygon or rectangle onto a blank canvas using the class index as fill value, and apply the result as a per-pixel label map.