| --- |
| 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} |
| } |
| ``` |
