TimJaspersTue/SurgeNetYoutube

Surgical Youtube Dataset

Welcome to the Surgical YouTube Dataset! This curated collection of surgical video frames is a key contribution of our research, playing a role in enhancing the performance of our foundational model, SurgeNetXL.

The dataset is derived from 680 hours of surgical video footage obtained from YouTube, processed using the code from Schmidgall et al. (2024) and sampled at 1 frame per second (fps). Through manual annotation, we ensured the removal of all non-minimally invasive procedures and out-of-body frames, resulting in a dataset comprising 2,074,234 frames across 23 distinct surgical procedures.

The Surgical YouTube Dataset is publicly available and serves as a key component of our SurgeNetXL model, which also integrates several other open-source datasets. These additional datasets are detailed in the table below.

Models trained on this dataset, including SurgeNetXL, can be found on github.

Procedure-specific subset	Dataset	Procedure	#videos	#frames	Public
SurgeNetCholec	Cholec80 (Twinnanda et al., 2017b)	Laparoscopic Cholecystectomy	76	179,164	Yes
	HeiChole (Maier-Hein et al., 2021)	Laparoscopic Cholecystectomy	30	53,427	Yes
	hSDB-Chole (Yoon et al., 2021)	Laparoscopic Cholecystectomy	24	18,064	Yes
SurgeNetRAMIE	RAMIE-UMCU	RA Esophagectomy	28	377,287	No
SurgeNetRARP	ESAD Bawa et al., 2021	RA Esophagectomy	28	47,282	Yes
	PSI-AVA Valderrama et al., 2022	RA Prostatectomy	8	73,618	Yes
	RARP-AvL	RA Prostatectomy	8	261,516	No
Others	DSAD (Carstens et al., 2023)	RA Rectal Resection/Extirpation	32	14,623	Yes
	GLENDA (Leibetseder et al., 2020)	Gynecologic Laparoscopy	400	25,682	Yes
	LapGyn4 (Leibetseder et al., 2018)	Gynecologic Laparoscopy	500	59,616	Yes
	MultiBypass140 (Lavanchy et al., 2024)	Laparoscopic Gastric Bypass Surgery	140	749,419	Yes
	hSDB-Gastric (Yoon et al., 2021)	RA Gastrectomy	24	35,576	Yes
	SurgToolLoc2022 (Zia et al., 2023)	11 different RA porcine procedures	N/A	741,516	Yes
	YouTube ours	23 identified procedures	3,253	2,074,234	Yes

SurgeNetXL variations	Dataset	Procedure	#videos	#frames	Public
SurgeNetSmall	10% of the above (excluding YouTube)	All of the above (excluding YouTube)	>1345	263,679	Partly
SurgeNetPublic	All public datasets (excluding YouTube & private datasets)	All of the above (excluding YouTube & RA Esophagectomy)	>1238	1,997,987	Yes
SurgeNet	All of the above (excluding YouTube)	All of the above (excluding YouTube)	>1345	2,636,790	Partly
SurgeNetXL	All of the above	All of the above	>4598	4,711,024	Partly

Publication

Scaling up self-supervised learning for improved surgical foundation models

Tim J.M. Jaspers^{1* :email:}, Ronald L.P.D. de Jong^2*, Yiping Li², Carolus H.J. Kusters¹, Franciscus H.A. Bakker⁵, Romy C. van Jaarsveld³, Gino M. Kuipers³, Richard³, Jelle P. Ruurda³, Willem M. Brinkman⁴, Josien P.W. Pluim², Peter H.N. de With¹, Marcel Breeuwer², Yasmina Al Khalil², Fons van der Sommen¹

¹ Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology
² Department of Biomedical Engineering, Medical Image Analysis, Eindhoven University of Technology, Eindhoven, The Netherlands
³ Department of Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
⁴ Department of Oncological Urology, University Medical Center Utrecht, Utrecht, The Netherlands
⁵ Department of Surgery, Catharina Hospital, Eindhoven, The Netherlands

^* Both authors attributed equally
(^:email:) corresponding author

arxiv
(Article)

Abstract

Foundation models have revolutionized computer vision by achieving state-of-the-art performance across diverse tasks through large-scale pretraining on extensive datasets. However, their application in surgical computer vision has been limited. This study addresses this gap by introducing SurgeNetXL, a novel surgical foundation model that sets a new benchmark in surgical computer vision. Trained on the largest reported surgical dataset to date, comprising over 4.7 million video frames, SurgeNetXL achieves consistent top-tier performance across six datasets spanning four surgical procedures and three tasks, including semantic segmentation, phase recognition, and critical view of safety (CVS) classification. Compared to the best-performing surgical foundation models, SurgeNetXL shows mean improvements of 0.26%, 8.95%, and 12.6% for semantic segmentation, phase recognition, and CVS classification, respectively. Additionally, SurgeNetXL outperforms the best-performing ImageNet-based variants by 10.3%, 4.0%, and 1.6% in the respective tasks. In addition to advancing model performance, this work provides key insights into scaling pretraining datasets, extending training durations, and optimizing model architectures specifically for surgical computer vision. These findings pave the way for improved generalizability and robustness in data-scarce scenarios, offering a comprehensive framework for future research in this domain.

Results

The following figures are from our publication, showcasing the performance of our introduced foundation model across diverse surgical tasks and procedures. These results demonstrate the model’s state-of-the-art performance on a variety of downstream tasks, reflecting its versatility and robustness in handling datasets from multiple surgical procedures.

Figure 1 and Figure 2 illustrate comparative rankings of our model against existing benchmarks, highlighting its superior generalization capabilities across datasets. Figure 3 provides a t-SNE visualization, showcasing the clear cluster separation per specific dataset achieved by the model’s feature embeddings, further emphasizing its effectiveness in capturing meaningful representations.

Fig 1: Radar chart showing model ranks across datasets.

Fig 2: Blob chart representing ranking metrics for models.

Fig 3: t-SNE visualization of feature embeddings showing cluster separation across datasets.

Citation

If you use this Surgical Youtube dataset or one of our models please cite our paper:

@misc{jaspers2025scalingselfsupervisedlearningimproved,
      title={Scaling up self-supervised learning for improved surgical foundation models}, 
      author={Tim J. M. Jaspers and Ronald L. P. D. de Jong and Yiping Li and Carolus H. J. Kusters and Franciscus H. A. Bakker and Romy C. van Jaarsveld and Gino M. Kuiper and Richard van Hillegersberg and Jelle P. Ruurda and Willem M. Brinkman and Josien P. W. Pluim and Peter H. N. de With and Marcel Breeuwer and Yasmina Al Khalil and Fons van der Sommen},
      year={2025},
      eprint={2501.09436},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.09436}, 
}