LASANA: Laparoscopic Skill Analysis and Assessment Video Dataset

The LASANA dataset provides 1,270 trimmed and synchronized stereo video recordings of four basic laparoscopic training tasks performed by 70 participants (58 medical students and 12 clinicians) in a Laparo Aspire training box. Each recording is annotated with a GOALS-inspired structured skill rating aggregated from three independent raters, as well as binary labels indicating the presence or absence of task-specific procedural errors. Predefined participant-level train/validation/test splits are provided for reproducible benchmarking of video-based skill assessment and error recognition methods.

Tasks

Peg transfer (329 videos)

Circle cutting (311 videos)

Balloon resection (316 videos)

Suture & knot (314 videos)

Task	Description	Videos	Mean Duration
Peg transfer	Transfer six triangular objects from the left to the right side of a pegboard, then transfer them back	329	2 min 32 s
Circle cutting	Accurately cut along a pre-marked circular path on a piece of gauze	311	3 min 32 s
Balloon resection	Carefully incise the outer balloon without puncturing the inner balloon, which is filled with water	316	3 min 55 s
Suture & knot	Pass a suture through a Penrose drain and close the slit with a laparoscopic knot consisting of three throws	314	4 min 30 s

Peg transfer, circle cutting, and suture & knot are adapted from the MISTELS curriculum. The balloon resection task was developed at the University Hospital Carl Gustav Carus, Dresden. Recordings are 960×540 stereo at 20 fps, captured with a Karl Storz TIPCAM 1 S 3D LAP 30° endoscope.

Annotations

Each recording is annotated with a Global Rating Score (GRS) computed as the sum across four GOALS-inspired skill aspects rated on a 5-point Likert scale: depth perception, efficiency, bimanual dexterity, and tissue handling. Three independent raters score each video; their scores are normalized per rater and averaged to produce the final aggregated rating. Average pairwise inter-rater agreement (Lin's Concordance Correlation Coefficient) exceeds 0.65 for all tasks except circle cutting (0.49). The raw per-rater scores are also provided as rater{0..3}_* columns.

Each task additionally has binary labels for task-specific procedural errors:

Task	Errors
Peg transfer	object_dropped_within_fov, object_dropped_outside_of_fov
Circle cutting	cutting_imprecise, gauze_detached
Balloon resection	cutting_imprecise, cutting_incomplete, balloon_opened, balloon_damaged, balloon_perforated
Suture & knot	needle_dropped, suture_imprecise, fewer_than_three_throws, slit_not_closed, knot_comes_apart, drain_detached

Splits

Participant-level splits (approximate 75:10:15 ratio) ensure that all recordings from a single participant appear in only one subset:

Task	Total	Train	Validation	Test
Peg transfer	329	243	32	54
Circle cutting	311	234	27	50
Balloon resection	316	235	30	51
Suture & knot	314	232	32	50

Quick Start

from datasets import load_dataset

# Load a task: peg_transfer, circle_cutting, balloon_resection, or suture_and_knot
ds = load_dataset("nct-tso/lasana", "peg_transfer")

# Iterate over training split
for sample in ds["train"]:
    left_video = sample["left"]     # torchcodec VideoDecoder
    right_video = sample["right"]   # torchcodec VideoDecoder
    grs = sample["grs"]             # aggregated Global Rating Score
    # ... all annotation columns available

# Load all four tasks
tasks = ["peg_transfer", "circle_cutting", "balloon_resection", "suture_and_knot"]
all_ds = {task: load_dataset("nct-tso/lasana", task) for task in tasks}

# Stream without downloading everything
ds = load_dataset("nct-tso/lasana", "suture_and_knot", split="train", streaming=True)

Downloading raw files

from huggingface_hub import snapshot_download

# Download the entire dataset (~175 GB)
snapshot_download("nct-tso/lasana", repo_type="dataset", local_dir="./lasana")

# Download only a specific task
snapshot_download("nct-tso/lasana", repo_type="dataset", local_dir="./lasana",
                  allow_patterns="peg_transfer/**")

# Download only left camera videos across all tasks
snapshot_download("nct-tso/lasana", repo_type="dataset", local_dir="./lasana",
                  allow_patterns="*/*/left/*")

Stereo calibration

Camera intrinsics, distortion coefficients, and stereo extrinsics for both cameras are provided in camera_calibration.yaml at the repository root. Sample code for image rectification and stereo matching is available in the stereo matching repository.

Citation

@article{funke2026lasana,
  title   = {A benchmark for video-based laparoscopic skill analysis and assessment},
  author  = {Funke, Isabel and Bodenstedt, Sebastian and von Bechtolsheim, Felix and Oehme, Florian and Maruschke, Michael and Herrlich, Stefanie and Weitz, J{\"u}rgen and Distler, Marius and Mees, S{\"o}ren Torge and Speidel, Stefanie},
  year    = {2026},
  eprint  = {2602.09927},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  doi     = {10.25532/OPARA-1046},
  url     = {https://arxiv.org/abs/2602.09927}
}