---
license: cc-by-nc-sa-4.0
task_categories:
- image-segmentation
tags:
- medical
- surgical
- endoscopy
- robot-assisted-surgery
- prostatectomy
- instrument-segmentation
pretty_name: SAR-RARP50
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: operation_id
    dtype: int32
  - name: video_id
    dtype: string
  - name: part
    dtype: int32
  - name: frame_index
    dtype: int32
  - name: time_sec
    dtype: float32
  - name: action_label
    dtype: int32
  - name: action_name
    dtype: string
  - name: challenge_subset
    dtype: string
  - name: num_frames_video
    dtype: int32
  splits:
  - name: train
    num_bytes: 13529331109
    num_examples: 13043
  - name: test
    num_bytes: 3011187155
    num_examples: 3252
  download_size: 16541661335
  dataset_size: 16540518264
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# SAR-RARP50

Surgical instrument segmentation and action recognition on **50 robot-assisted
radical prostatectomy (RARP)** procedures — the MICCAI/EndoVis 2022 SAR-RARP50
challenge dataset. Video covers the dorsal venous complex (DVC) suturing phase,
captured from the left channel of a da Vinci stereo endoscope.

## Contents

One row per **annotated frame**: the decoded 1920x1080 RGB video frame and its
instrument segmentation mask.

| split | operations | archives | frames |
|-------|-----------|----------|--------|
| train | 40 | 44 | 13,043 |
| test  | 10 | 10 |  3,252 |
| **total** | **50** | **54** | **16,295** |

Masks are annotated at **1 Hz** from 60 fps video, which is exactly the rate the
official challenge metric scores at, so this mirror is the complete segmentation
benchmark. Both splits carry real ground truth. The raw `video_left.avi` files
are not mirrored (31 GiB, ~98% unannotated); the 10 Hz action labels are kept in
full, both as per-row columns and as `action_annotations.parquet` (162,705 rows).

## Segmentation classes

Mutually exclusive; stored single-channel, values 0-9.

| id | class | id | class |
|----|-------|----|-------|
| 0 | Background | 5 | Thread |
| 1 | Tool clasper | 6 | Suction tool |
| 2 | Tool wrist | 7 | Needle Holder |
| 3 | Tool shaft | 8 | Clamps |
| 4 | Suturing needle | 9 | Catheter |

## Action classes (0-7)

`0` Other · `1` Picking-up the needle · `2` Positioning the needle tip ·
`3` Pushing the needle through the tissue · `4` Pulling the needle out of the
tissue · `5` Tying a knot · `6` Cutting the suture · `7` Returning/dropping the
needle

## Columns

| column | notes |
|--------|-------|
| `image` / `mask` | 1920x1080; mask values 0-9 |
| `operation_id` | 1-50. **Group on this** — it collapses the two-part operations |
| `video_id` | source archive, e.g. `video_11_1` (keeps part identity) |
| `part` | 1 or 2 for operations 11/15/17/29, else null |
| `frame_index` | 0-based index into the original `video_left.avi` |
| `time_sec` | `frame_index / 60` |
| `action_label` / `action_name` | 10 Hz action annotation at this frame |
| `challenge_subset` | `training_set_1` / `training_set_2` / `test` |
| `num_frames_video` | total frames in the source video |

## Differences from the upstream release

This mirror is a faithful reformatting, with four upstream inconsistencies fixed.
Each was verified against the actual bytes rather than the documentation.

1. **Masks are stored single-channel.** Upstream PNGs are 3-channel RGB with
   R==G==B despite the README calling them "grayscale". Channel 0 is kept —
   lossless, and ~2.7x smaller.
2. **Action labels are 0-7.** The upstream README table lists 1-8; it is off by
   one (its header row is copy-pasted from the segmentation table). The values in
   `action_discrete.txt` are 0-7, matching the paper, the challenge
   `GestureList.txt`, and the official evaluator's `n_classes=8`.
3. **`training_set_2` is corrected.** The upstream README duplicates video 34 and
   omits video 14, so its two training lists cover 39 of 40 operations. Video 14
   is restored, per the original challenge listing.
4. **Operations 11/15/17/29 ship as two archives each** whose frame numbering
   restarts at 0. `video_id` keeps the parts distinct while `operation_id`
   collapses them, so grouping on `operation_id` never splits one patient across
   folds — the challenge explicitly treats these as one operation.

## Caveats

- **Class imbalance is severe.** Needle Holder appears in ~4% of frames and
  Suction tool in ~9%; pixel imbalance reaches ~1000:1. The official metric uses
  `MeanIoU(ignore_empty=False)`, which awards 1.0 when a class is absent from
  *both* prediction and reference — so published scores (winner: 0.829 mIoU) are
  inflated relative to a mean-IoU-over-present-classes and are not directly
  comparable unless that convention is replicated.
- **201 masks (1.23%) are entirely background**, 17 of them in test.
- **Action class 5 ("Tying a knot") is effectively untestable**: it appears in
  one test video only (video_46, 186 frames).
- Video is lossy MPEG-4 Part 2 / yuv420p; frames here are a lossless PNG
  encoding of that already-compressed source.

## Overlap with other datasets

No patient overlap with EndoVis 2017/2018 (both porcine) or with
CholecSeg8k / m2caiSeg (cholecystectomy). Shares patients with **RARP45** (never
publicly released) and with the **SEDMamba** surgical-error dataset
(UCL RDR `10.5522/04/27992702`), which re-annotates 48 of these same videos —
do not benchmark against SEDMamba as if it were independent.

## License

**CC BY-NC-SA 4.0**, inherited from both source deposits. ShareAlike applies: this
reformatted derivative carries the same license. Non-commercial use only.

Note: third-party Kaggle mirrors of this dataset declare ODbL, which is incorrect
and more permissive than the authors' terms.

## Source

- Train: https://rdr.ucl.ac.uk/articles/dataset/SAR-RARP50_train_set/24932529 (DOI `10.5522/04/24932529`)
- Test: https://rdr.ucl.ac.uk/articles/dataset/SAR-RARP50_test_set/24932499 (DOI `10.5522/04/24932499`)
- Toolkit: https://github.com/surgical-vision/SAR_RARP50-evaluation

## Citation

```bibtex
@article{psychogyios2024sarrarp50,
  title   = {SAR-RARP50: Segmentation of surgical instrumentation and Action
             Recognition on Robot-Assisted Radical Prostatectomy Procedures},
  author  = {Psychogyios, Dimitrios and Colleoni, Emanuele and Van Amsterdam,
             Beatrice and others},
  journal = {arXiv preprint arXiv:2401.00496},
  year    = {2024},
  doi     = {10.48550/arXiv.2401.00496}
}
```