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arxiv: 2507.00209 · v3 · submitted 2025-06-30 · 📡 eess.IV · cs.AI· cs.CV· cs.RO

SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures

Fengyi Jiang , Xiaorui Zhang , Lingbo Jin , Ruixing Liang , Yuxin Chen , Adi Chola Venkatesh , Jason Culman , Tiantian Wu
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Lirong Shao Wenqing Sun Cong Gao Hallie McNamara Jingpei Lu Omid Mohareri
This is my paper

Pith reviewed 2026-05-19 06:15 UTC · model grok-4.3

classification 📡 eess.IV cs.AIcs.CVcs.RO
keywords 4K endoscopic videorobotic surgery datasetminimally invasive proceduressurgical computer visionsuper resolutioninstrument detectiondepth estimationsmoke removal
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The pith

SurgiSR4K introduces the first public native 4K resolution dataset for endoscopic videos in robotic-assisted surgery.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes SurgiSR4K as a new resource that supplies endoscopic videos and images at native 4K resolution for research in robotic minimally invasive surgery. Prior datasets fell short because they lacked this resolution despite the widespread use of 4K systems in clinical practice. By including realistic elements such as reflections from tools, tissue bleeding, and deformations, the dataset targets the exact visual problems surgeons encounter. This matters because higher resolution data can improve the training of algorithms for key tasks including instrument detection, depth estimation, and smoke removal in surgical settings. Public release of the data allows broader development of intelligent systems to support safer image-guided procedures.

Core claim

We introduce SurgiSR4K, the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution, representing realistic conditions of robotic-assisted procedures. It comprises diverse visual scenarios including specular reflections, tool occlusions, bleeding, and soft tissue deformations, meticulously designed to reflect common challenges faced during laparoscopic and robotic surgeries.

What carries the argument

The SurgiSR4K dataset serves as the central object, providing high-resolution endoscopic data that enables a range of computer vision applications in surgery by supplying realistic examples of intraoperative visual conditions.

If this is right

  • Enables training of super-resolution models using actual surgical 4K data rather than simulated or lower-res sources.
  • Supports development of smoke removal techniques that handle high-resolution surgical scenes.
  • Facilitates accurate surgical instrument detection and instance segmentation with finer details available.
  • Allows for improved 3D tissue reconstruction, monocular depth estimation, and novel view synthesis from endoscopic views.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could lead to studies measuring the performance gains from using 4K data over standard HD in surgical AI models.
  • Researchers in related fields like general endoscopy might adapt the dataset collection methods for their own high-res needs.
  • The dataset opens opportunities to explore how high resolution affects the training of vision-language models for surgical guidance.
  • Potential extension to real-time applications where the high-res data is processed during live robotic procedures.

Load-bearing premise

The scenarios and visual challenges in the dataset were designed to accurately reflect common conditions in actual laparoscopic and robotic surgeries.

What would settle it

If comparisons with real surgical footage reveal that the dataset lacks sufficient examples of key challenges like bleeding or tissue deformation, or if the resolution is not truly native 4K in practice.

Figures

Figures reproduced from arXiv: 2507.00209 by Adi Chola Venkatesh, Cong Gao, Fengyi Jiang, Hallie McNamara, Jason Culman, Jingpei Lu, Lingbo Jin, Lirong Shao, Omid Mohareri, Ruixing Liang, Tiantian Wu, Wenqing Sun, Xiaorui Zhang, Yuxin Chen.

Figure 1
Figure 1. Figure 1: Side-by-side comparison of 1080p (top) and 4K (bottom) endoscopic images captured simultaneously [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example frames from the training dataset, showcasing various tools used in different scenarios. These [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of image quality at different resolutions. From left to right: native 4K image (3840 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Frame-by-frame example of a 5-second video clip sampled at 2 fps, showing training data with a bipolar [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Each recorded sequence spans several minutes [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 5
Figure 5. Figure 5: Experimental setup for SurgiSR4K data col [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Examples of downstream applications: (a) instance segmentation (Ravi et al. (2024)), (b) surgical tool [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original abstract

High-resolution imaging is crucial for enhancing visual clarity and enabling precise computer-assisted guidance in minimally invasive surgery (MIS). Despite the increasing adoption of 4K endoscopic systems, there remains a significant gap in publicly available native 4K datasets tailored specifically for robotic-assisted MIS. We introduce SurgiSR4K, the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution, representing realistic conditions of robotic-assisted procedures. SurgiSR4K comprises diverse visual scenarios including specular reflections, tool occlusions, bleeding, and soft tissue deformations, meticulously designed to reflect common challenges faced during laparoscopic and robotic surgeries. This dataset opens up possibilities for a broad range of computer vision tasks that might benefit from high resolution data, such as super resolution (SR), smoke removal, surgical instrument detection, 3D tissue reconstruction, monocular depth estimation, instance segmentation, novel view synthesis, and vision-language model (VLM) development. SurgiSR4K provides a robust foundation for advancing research in high-resolution surgical imaging and fosters the development of intelligent imaging technologies aimed at enhancing performance, safety, and usability in image-guided robotic surgeries.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces SurgiSR4K as the first publicly accessible native 4K endoscopic video dataset for robotic-assisted minimally invasive surgery. It comprises diverse scenarios with challenges including specular reflections, tool occlusions, bleeding, and soft tissue deformations, positioned to support computer vision tasks such as super-resolution, smoke removal, instrument detection, 3D reconstruction, depth estimation, segmentation, novel view synthesis, and vision-language modeling.

Significance. If the dataset is released with full documentation and its novelty is substantiated, it could address a documented gap in high-resolution surgical imaging resources and enable improved algorithm development for image-guided robotic procedures. The absence of collection details and prior-work comparisons currently prevents assessment of whether this contribution is load-bearing or incremental.

major comments (2)
  1. [Abstract] Abstract: The central claim that SurgiSR4K is 'the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution' is unsupported by any enumeration or table comparing resolutions, access status, and release dates of prior datasets (e.g., EndoVis, Cholec80 variants, or MICCAI releases). This omission directly undermines the uniqueness assertion.
  2. [Abstract] Abstract: The assertion that scenarios were 'meticulously designed to accurately reflect common conditions in actual laparoscopic and robotic surgeries' provides no information on collection protocol, camera calibration, patient consent, total dataset size (videos, frames, or procedures), or any validation that the captured scenes are realistic. These omissions are load-bearing for the dataset's claimed utility.
minor comments (1)
  1. [Abstract] The abstract lists supported tasks but does not indicate how native 4K resolution specifically benefits each (e.g., quantitative gains in depth estimation or segmentation). Adding one sentence with expected advantages would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review of our manuscript on the SurgiSR4K dataset. The comments highlight important areas for strengthening the presentation of novelty and data collection details. We address each point below and will incorporate revisions to improve clarity and substantiation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that SurgiSR4K is 'the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution' is unsupported by any enumeration or table comparing resolutions, access status, and release dates of prior datasets (e.g., EndoVis, Cholec80 variants, or MICCAI releases). This omission directly undermines the uniqueness assertion.

    Authors: We agree that an explicit comparison would better support the novelty claim. In the revised manuscript, we will add a dedicated table in the introduction or related work section that enumerates prior surgical video datasets (including EndoVis, Cholec80 and its variants, and relevant MICCAI releases), detailing their resolutions, public accessibility status, release dates, and key characteristics. This will directly substantiate that no prior publicly available dataset provides native 4K resolution for robotic-assisted minimally invasive procedures. revision: yes

  2. Referee: [Abstract] Abstract: The assertion that scenarios were 'meticulously designed to accurately reflect common conditions in actual laparoscopic and robotic surgeries' provides no information on collection protocol, camera calibration, patient consent, total dataset size (videos, frames, or procedures), or any validation that the captured scenes are realistic. These omissions are load-bearing for the dataset's claimed utility.

    Authors: We acknowledge that the current manuscript lacks sufficient detail on these aspects, which are important for assessing the dataset's realism and utility. In the revision, we will expand the methods or data description section to include: (1) the collection protocol and setup, (2) camera calibration procedures, (3) total dataset statistics (number of videos, frames, and procedures), and (4) any validation steps or expert review confirming that the scenarios reflect common surgical challenges. Regarding patient consent and ethics, we will clarify whether the data originates from real procedures, phantoms, or ex-vivo models and include relevant approvals or statements as appropriate. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset release without derivations or self-referential claims

full rationale

This is a dataset paper whose value rests on the collection and public release of native 4K endoscopic video. No equations, predictions, fitted parameters, or derivation chains appear in the provided text. The claim of being the 'first publicly accessible' dataset is an empirical statement about the literature rather than a mathematical reduction to the paper's own inputs. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results are present. The paper is self-contained as a data contribution and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Dataset release papers rest primarily on empirical collection choices rather than mathematical axioms or new entities.

axioms (1)
  • domain assumption A significant gap exists in publicly available native 4K datasets for robotic-assisted MIS
    Stated directly in the abstract as motivation for the work.

pith-pipeline@v0.9.0 · 5802 in / 1164 out tokens · 69783 ms · 2026-05-19T06:15:07.333038+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 5 internal anchors

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