--- license: cc-by-nc-sa-4.0 pretty_name: SurgBox task_categories: - object-detection tags: - medical - surgery - laparoscopy - cholecystectomy - cholec80 - surgical-instruments size_categories: - 100K- SurgBox contains annotations only. The Cholec80 videos are distributed by CAMMA under their own terms, and SurgBox may be used for non-commercial purposes only (CC BY-NC-SA 4.0). extra_gated_fields: Name: text Affiliation: text I have obtained, or will request, the Cholec80 videos from CAMMA: checkbox I will use SurgBox for non-commercial purposes only: checkbox --- # SurgBox Instance-level bounding boxes for surgical instruments in all 80 videos of Cholec80. **Paper:** *From Presence Labels to Bounding Boxes: Can Multimodal Large Language Models Scale Surgical Instrument Localization?* (MICAD 2026)
**Code:** [github.com/M-Hamdy-M/SurgBox](https://github.com/M-Hamdy-M/SurgBox)
**Models:** [SurgBox-YOLOv8-x](https://huggingface.co/M-Hamdy/SurgBox-YOLOv8-x), [SurgBox-RT-DETR-L](https://huggingface.co/M-Hamdy/SurgBox-RT-DETR-L), [SurgBox-YOLO11-x](https://huggingface.co/M-Hamdy/SurgBox-YOLO11-x) (paper, Table 2) and [SurgBox-Cross-Dataset](https://huggingface.co/M-Hamdy/SurgBox-Cross-Dataset) (Table 3) Cholec80 annotates, once per second, which of seven instruments are present, but not where they are. SurgBox converts these presence labels into class-specific bounding boxes using a multi-stage multimodal LLM pipeline, and provides a 1,000-frame human reference, annotated twice and reviewed by a surgeon, for measuring localization quality. This repository contains annotations only. The videos must be requested from [CAMMA](http://camma.u-strasbg.fr/datasets). ## Configurations | Config | Split | Videos | Frames | Boxes | |---|---|:---:|:---:|:---:| | `surgbox` | train | 56 | 128,172 | 201,596 | | | validation | 8 | 16,971 | 25,428 | | | test | 16 | 39,355 | 58,953 | | `human_reference` | test | 80 | 1,000 | 1,313 | Boxes per class in `surgbox`: | Grasper | Bipolar | Hook | Scissors | Clipper | Irrigator | SpecimenBag | |:---:|:---:|:---:|:---:|:---:|:---:|:---:| | 139,464 | 9,010 | 103,495 | 4,164 | 6,708 | 11,295 | 11,841 | ```python from datasets import load_dataset surgbox = load_dataset("M-Hamdy/SurgBox") # train, validation, test human = load_dataset("M-Hamdy/SurgBox", "human_reference", split="test") ``` Loading requires `datasets` 4.0 or later. Accept the conditions on this page and log in with `hf auth login` first. ## Fields Each row is one frame. | Field | Description | |---|---| | `image_id` | image id in `coco/surgbox.json.gz` (`surgbox`) or `coco/human/*.json` (`human_reference`); the two differ, so join the configs on `video` and `frame` | | `file_name` | `video01/000025.png`, the frame's path after extraction (below) | | `video` | Cholec80 video number, 1 to 80 | | `frame` | frame index in the original 25 fps video, matching the `Frame` column of the Cholec80 tool annotations | | `width`, `height` | 854×480, except videos 78, 79 and 80 (1920×1080) | | `objects` | `category`, `bbox`, `area` and `confidence` of each box (`surgbox` only) | | `reference`, `annotator_1`, `annotator_2` | `category`, `bbox` and `area` of each box (`human_reference` only) | | `review` | surgeon review of the reference frame (`human_reference` only) | Frames are sampled at 1 fps. Boxes are `[x, y, width, height]` in pixels of the source video. A box covers the visible extent of the instrument, shaft and tip, without completing occluded or out-of-frame parts. Some boxes (423 of 285,977) extend past the image border; before training, clip boxes to the image and drop any side shorter than 2 pixels, as in the detection benchmark of the paper. `confidence` (1–5) is the MLLM's own confidence in each box. `category` is one of Grasper, Bipolar, Hook, Scissors, Clipper, Irrigator and SpecimenBag, stored as a class index (0–6) in the Parquet files and as `category_id` (1–7) in the COCO files. The same annotations are also provided in COCO format: ``` coco/ surgbox.json.gz all 184,498 frames human/ reference.json annotator 1 after surgeon review (the reference) annotator_1.json annotator 1 before review annotator_2.json annotator 2, independent pass splits.json video-level splits used in the paper ``` To extract the annotated frames from a Cholec80 video: ```python import cv2, os def extract(video, out_dir): os.makedirs(out_dir, exist_ok=True) cap, i = cv2.VideoCapture(video), 0 while (ok := cap.read())[0]: if i % 25 == 0: cv2.imwrite(f"{out_dir}/{i:06d}.png", ok[1]) i += 1 extract("video01.mp4", "video01") ``` ## Human reference The 1,000 frames span all 80 videos and are sampled from the Cholec80 labels to balance the seven classes, with 150 frames that have no instrument in the labels. Each frame was annotated by two annotators; the first annotation set was reviewed by a surgeon. `review` is `finalized` for the 984 frames the surgeon approved and `changes_requested` for 16 frames outside the laparoscopic view (e.g. out-of-body views), which have no boxes. | | Set F1 (%) | mIoU | Matched mIoU | |---|:---:|:---:|:---:| | Annotator 2 vs. reference (human agreement) | 94.18 | 0.814 | 0.912 | | Pipeline, evaluation run (paper, Table 1) | 94.66 | 0.784 | 0.857 | | `surgbox` (this release) | 94.42 | 0.774 | 0.852 | The released annotations are a separate run of the same pipeline over all frames; the pipeline is stochastic, so they score slightly differently on the reference than the evaluation run reported in the paper. Boxes are matched greedily within each class by IoU, and an unmatched reference box counts as zero. The evaluation script is in the [GitHub repository](https://github.com/M-Hamdy-M/SurgBox). The reference frames come from all 80 videos, 702 of them from training videos. To evaluate a detector trained on the `train` split, use the 197 frames from test videos with `review` = `finalized` (238 boxes), the expert-verified subset in the paper (Table 2). ## Splits The `surgbox` splits are video-disjoint (56 / 8 / 16 videos). `coco/splits.json` also lists the five CholecSeg8k test videos (28, 43, 48, 52, 55) used in the cross-dataset experiment; four of them are in `train` and were excluded from training in that experiment. The detection benchmark in the paper omits one test frame (video 44, frame 69,525), so its test split has 39,354 frames instead of 39,355. ## Citation **If you use SurgBox, please cite our paper:** ```bibtex @inproceedings{hamdy2026surgbox, title = {From Presence Labels to Bounding Boxes: Can Multimodal Large Language Models Scale Surgical Instrument Localization?}, author = {Hamdy, Mohamed and Abdel-Ghani, Muraam and Ahmed, Fatmaelzahraa and Nasar, Sifna and Ahmed, Mariam and Al-Jalham, Khalid and Al-Ali, Abdulaziz and Balakrishnan, Shidin}, booktitle = {Medical Imaging and Computer-Aided Diagnosis (MICAD)}, year = {2026} } ```
Source datasets (please cite these in addition to SurgBox) SurgBox is built on the Cholec80 videos and instrument-presence labels; the cross-dataset experiment uses CholecSeg8k. ```bibtex @article{twinanda2017endonet, title = {EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos}, author = {Twinanda, Andru P. and Shehata, Sherif and Mutter, Didier and Marescaux, Jacques and de Mathelin, Michel and Padoy, Nicolas}, journal = {IEEE Transactions on Medical Imaging}, volume = {36}, number = {1}, pages = {86--97}, year = {2017} } @article{hong2020cholecseg8k, title = {CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80}, author = {Hong, W.-Y. and Kao, C.-L. and Kuo, Y.-H. and Wang, J.-R. and Chang, W.-L. and Shih, C.-S.}, journal = {arXiv preprint arXiv:2012.12453}, year = {2020} } ```
## License The annotations are released for **non-commercial use only**, under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/), consistent with Cholec80 and CholecSeg8k. Cholec80 is not redistributed and remains subject to its own terms. ## Acknowledgements This work was supported by the Qatar Research Development and Innovation Council (QRDI), grant ARG01-0522-230266. ## Contact Questions, issues and suggestions are welcome. Please open a discussion on this page or [contact me](mailto:mm1905748@qu.edu.qa).