Blame for README.md

---
pretty_name: SegSTRONG-C
license: cc-by-nc-sa-4.0
viewer: false
task_categories:
- image-segmentation
size_categories:
- 10K<n<100K
tags:
- surgery
- surgical-tool-segmentation
- robustness
- corruption
- stereo
- endovis
- miccai
---

# SegSTRONG-C

**Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions**, the EndoVis'24 challenge at MICCAI 2024, organized by the ARCADE Lab at Johns Hopkins University.

[Challenge website](https://segstrongc.cs.jhu.edu/) · [Paper (arXiv:2407.11906)](https://arxiv.org/abs/2407.11906) · [Code and baselines (CaRTS)](https://github.com/hding2455/CaRTS)

## Overview

SegSTRONG-C asks how robust surgical tool segmentation is to realistic, non-adversarial corruptions. The task is binary segmentation of robot-assisted surgical tools against the tissue background, in stereo endoscopic images that can also be treated as video.

The data is paired by robotic replay: the same tool motion is recorded clean and again under smoke, bleeding, low brightness and an alternative background. Every condition of a subsequence therefore shows the same instant at each frame index and shares one ground-truth mask. Models train on clean data only and are evaluated on the corrupted versions.

## Contents

| Split | Sequence / subsequences | Conditions | Stereo frames | Images (incl. masks) | Size |
|---|---|---|---|---|---|
| train | 3/0, 3/2, 4/0, 4/1, 4/2, 5/0, 5/2, 7/0, 7/1, 8/1, 8/2 | regular | 3,300 | 13,200 | 22.5 GB |
| val | 1/0, 1/1, 1/2 | regular, smoke, blood, low_brightness, bg_change | 900 per condition | 10,800 | 30.1 GB |
| test | 9/0, 9/1, 9/2 | regular, smoke, blood, low_brightness, bg_change | 900 per condition | 10,800 | 29.9 GB |

Every subsequence has 300 frames, each with a left and a right view.

The challenge used part of the val and test folders:
- **Validation:** `regular` and `bg_change`. That's 3,600 frames: 3 × 300 × 2 views × 2 conditions.
- **Ranked test:** `smoke`, `blood` and `low_brightness`. That's 5,400 frames: 3 × 300 × 2 views × 3 conditions.

The release includes every condition for both splits.

## Layout

```
SegSTRONGC_train/<seq>/<subseq>/regular/{left,right}/<frame>.png
SegSTRONGC_train/<seq>/<subseq>/ground_truth/{left,right}/<frame>.png
SegSTRONGC_val/val/<seq>/<subseq>/{regular,smoke,blood,low_brightness,bg_change}/{left,right}/<frame>.png
SegSTRONGC_val/val/<seq>/<subseq>/ground_truth/{left,right}/<frame>.png
SegSTRONGC_test/test/<seq>/<subseq>/{regular,smoke,blood,low_brightness,bg_change}/{left,right}/<frame>.png
SegSTRONGC_test/test/<seq>/<subseq>/ground_truth/{left,right}/<frame>.png
```

- **Images:** RGB PNG, 1920×1080, named `0.png` … `299.png`.
- **Masks** (`ground_truth`): 8-bit grayscale PNG, 1920×1080, **0 = background and 255 = tool**, so divide by 255 to get labels. Every mask contains some tool; tools average 13–20% of pixels, depending on the split.
- **Pairing:** `<frame>.png` in any condition folder of a subsequence pairs with `ground_truth/<side>/<frame>.png` of the same subsequence.

The files are the original release, unzipped from `SegSTRONGC_{train,val,test}.zip` with the folder structure unchanged.

## Quick start

```python
from huggingface_hub import snapshot_download
import numpy as np
from PIL import Image

# Download one split only (the full dataset is about 82 GB).
root = snapshot_download("hding2455/SegSTRONGC", repo_type="dataset",
                         allow_patterns=["SegSTRONGC_train/*"])

frame = f"{root}/SegSTRONGC_train/3/0"
image = np.array(Image.open(f"{frame}/regular/left/0.png"))           # (1080, 1920, 3) uint8
mask = np.array(Image.open(f"{frame}/ground_truth/left/0.png")) > 0   # (1080, 1920) bool, True = tool
```

For corrupted evaluation data, use for example `allow_patterns=["SegSTRONGC_test/test/9/*/smoke/*", "SegSTRONGC_test/test/9/*/ground_truth/*"]`.

## Evaluation

The challenge ranked entries by the Dice similarity coefficient (DSC) and normalized surface distance (NSD) at several tolerances, aggregated over the three corruption domains: smoke, bleeding and low brightness. Baseline models and evaluation code are in [CaRTS](https://github.com/hding2455/CaRTS).

## License

The SegSTRONG-C data is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/):
- use it with attribution (cite the paper below);
- for non-commercial purposes only;
- share derivatives under the same license.

The CaRTS code is licensed separately.

## Citation

```bibtex
@misc{ding2024segstrongcsegmentingsurgicaltools,
      title={SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge},
      author={Ding, Hao and Lu, Tuxun and Zhang, Yuqian and others},
      year={2024},
      eprint={2407.11906},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```