Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training

Ivo Wolf; Lalith Sharan; Matthias Karck; Raffaele De Simone; Sandy Engelhardt

arxiv: 1906.10011 · v1 · pith:ZW3535TWnew · submitted 2019-06-24 · 📡 eess.IV · cs.CV· cs.CY

Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training

Sandy Engelhardt , Lalith Sharan , Matthias Karck , Raffaele De Simone , Ivo Wolf This is my paper

Pith reviewed 2026-05-25 16:50 UTC · model grok-4.3

classification 📡 eess.IV cs.CVcs.CY

keywords conditional generative adversarial networksstereo consistencysurgical trainingimage-to-image synthesisendoscopic imageshyperrealismaugmented realityphantom imaging

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The pith

A cross-domain conditional GAN generates consistent stereo pairs for hyperrealistic surgical training images.

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

The paper introduces a conditional generative adversarial network conditioned across real intraoperative and phantom domains to transform training phantom images into realistic endoscopic views. Standard image-to-image methods can produce artifacts that disrupt stereo alignment and depth cues between left and right frames. The cross-domain conditioning aims to enforce consistency in the generated pairs. Human raters viewing the outputs on a 3D monitor rated the new method as preferred or equal in 84 of 90 cases for depth perception and realism. This matters for surgical training because phantoms can then supply more lifelike visual feedback during practice of cutting and suturing.

Core claim

We propose a cross-domain conditional generative adversarial network approach that generates more consistent stereo pairs from surgical training phantoms, yielding substantial improvements in depth perception and realism over the baseline as evaluated by domain experts and medical students.

What carries the argument

Cross-domain conditional GAN that learns mappings from phantom frames to realistic endoscopic images while enforcing consistency between stereo views.

If this is right

Uniform phantom textures are replaced by heterogeneous tissue appearances without introducing stereo artifacts.
Depth perception improves when the generated pairs are viewed in augmented reality on 3D monitors.
The outputs receive higher or equal subjective ratings than the baseline in 84 of 90 evaluations.
Phantoms become usable for more realistic endoscopic training scenarios that require accurate stereo vision.

Where Pith is reading between the lines

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

The same cross-domain conditioning principle could be tested on video sequences to maintain consistency across time.
Adding an explicit term for stereo disparity in the loss function might strengthen consistency beyond what the current architecture achieves.
The method could be applied to other paired medical imaging tasks where left-right or multi-view alignment is required.

Load-bearing premise

Subjective preference ratings from only six evaluators on ninety image pairs reliably indicate genuine improvements in stereo consistency and depth perception.

What would settle it

An objective test that measures average disparity error between the generated left and right images using a stereo matching algorithm and compares the error to the baseline method.

Figures

Figures reproduced from arXiv: 1906.10011 by Ivo Wolf, Lalith Sharan, Matthias Karck, Raffaele De Simone, Sandy Engelhardt.

**Figure 1.** Figure 1: Proposed architecture that shows the X→Y →X-cycle for a stereo pair (xl, xr). In contrast to classical generators, each generator G and F takes two inputs and generates one output. The second input image, e.g. yW , xT , is taken from the other domain and can be chosen randomly. To enable a better consistency, the output of G, which is y 0 l , is chosen as a second input in the generation cycle of the righ… view at source ↗

**Figure 1.** Figure 1: 2.2 Network Architectures The used network architectures of the generators and discriminators are largely the same as in the original CycleGAN approach [4]. A TensorFlow implementation provided on GitHub4 was used as the basis and extended. All discriminators take the complete input images, which is different from the 70 × 70 PatchGAN approach [4]. For the generators, 7 instead of 9 residual blocks are us… view at source ↗

**Figure 2.** Figure 2: Mono- and stereoscopic examples from mitral valve repair. The scenes are diverse: with or without prosthetic ring, sutures, instruments and needles, blood etc. pair frames from the training with the surgical phantom. To avoid overfitting of the model, valve replica shown in videos for network training were not used for network testing. Intraoperatively, more than 620,000 stereo pairs were captured during t… view at source ↗

**Figure 3.** Figure 3: Examples from CycleGAN baseline [4] and our proposed method. (10 instances are better by ∆2 and 29 are better by ∆1). In three instances, both methods were assessed as equally good and in three other instances, our method was rated worse in stereo consistency. Considering the evaluation by the expert, a similar picture can be drawn. The respective diagram on assessment of depth perception is provided in [… view at source ↗

**Figure 4.** Figure 4: Expert and non-expert ratings for depth perception and realism. Symbols indicate the rating per participant of the generated samples by cross-domain conditional GAN. Arrows show the difference to CycleGAN [4]. to the unpaired CycleGAN approach [4]. Due to conditioning on a second image, which is drawn from the target domain (real or generated content), the network is also able to generate images with less… view at source ↗

read the original abstract

Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are based on deep image-to-image transformation methods. Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences. Conditioned on frames from the surgical training process, the learned models are able to generate impressive results by transforming unrealistic parts of the image (e.g.\ the uniform phantom texture is replaced by the more heterogeneous texture of the tissue). Image-to-image synthesis usually learns a mapping $G:X~\to~Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$. However, it does not necessarily force the generated images to be consistent and without artifacts. In the endoscopic image domain this can affect depth cues and stereo consistency of a stereo image pair, which ultimately impairs surgical vision. We propose a cross-domain conditional generative adversarial network approach (GAN) that aims to generate more consistent stereo pairs. The results show substantial improvements in depth perception and realism evaluated by 3 domain experts and 3 medical students on a 3D monitor over the baseline method. In 84 of 90 instances our proposed method was preferred or rated equal to the baseline.

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.

Desk Editor's Note private letter to a colleague

They adapted a conditional GAN for stereo-consistent phantom-to-real endoscopic translation but supported it only with subjective ratings from six people.

read the letter

The main takeaway is that they built a conditional GAN to translate phantom endoscopic images to realistic ones while keeping stereo pairs consistent, and a handful of raters liked it better than the baseline most of the time. What is new is the emphasis on cross-domain conditioning to preserve stereo consistency in this medical setting. The paper points out that usual image-to-image GANs can create artifacts that ruin depth cues, which matters for 3D surgical training. They evaluate by showing pairs on a 3D monitor to three experts and three students, getting preference or tie in 84 out of 90 cases. It does a solid job applying the technique to a real need in phantom-based training and getting feedback from actual users in the domain. The soft spots are in the evaluation and the missing technical details. Six raters is a very small group for claiming substantial improvements, and without any numbers on disparity error or other objective stereo metrics, the preference could be driven by appearance rather than consistency. The description does not explain the exact changes to the model or training to enforce the stereo property. This is for specialists in medical image translation and surgical simulators. A reader working on similar GAN applications in healthcare could pick up the idea and try it, but would likely need to add their own validation. I would recommend sending it to peer review. The application is practical and the human study is a start, but referees should ask for objective measures and more on the method to see if it really delivers on the stereo claim.

Referee Report

1 major / 1 minor

Summary. The manuscript proposes a cross-domain conditional generative adversarial network (cGAN) to transform phantom endoscopic images into hyperrealistic surgical scenes while aiming to preserve stereo consistency for better depth perception. Conditioned on training phantom frames, the model learns mappings from real intraoperative sequences; evaluation by 3 domain experts and 3 medical students on a 3D monitor shows the proposed method preferred or rated equal to baseline in 84 of 90 instances.

Significance. If the stereo-consistency improvements are substantiated, the approach could meaningfully advance AR-based surgical training by replacing uniform phantom textures with heterogeneous tissue appearance without degrading binocular cues. The cross-domain conditioning and human preference protocol are standard for image-to-image tasks, but the absence of any quantitative stereo metric leaves the core technical contribution unanchored.

major comments (1)

[Abstract] Abstract: the claim that the method generates 'more consistent stereo pairs' and improves 'depth perception' rests entirely on subjective preference counts (84/90) from six raters; no left-right disparity error, no stereo-matching consistency term, no SSIM/PSNR on corresponding pairs, and no stereo-specific baseline are reported, so the central technical assertion lacks quantitative support.

minor comments (1)

[Abstract] Abstract: the evaluation protocol (how pairs were presented, whether raters were blinded, statistical testing of the 84/90 count) is not described.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The main concern is the lack of quantitative metrics supporting the claims of improved stereo consistency and depth perception. We respond to this point below and propose revisions to address it where appropriate.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that the method generates 'more consistent stereo pairs' and improves 'depth perception' rests entirely on subjective preference counts (84/90) from six raters; no left-right disparity error, no stereo-matching consistency term, no SSIM/PSNR on corresponding pairs, and no stereo-specific baseline are reported, so the central technical assertion lacks quantitative support.

Authors: We agree that the evaluation is based on subjective preferences from six raters viewing stereo pairs on a 3D monitor. This approach was chosen because it directly measures the impact on depth perception in a manner relevant to surgical training. In the cross-domain conditional GAN setting, quantitative metrics such as left-right disparity error are not straightforward to compute due to the absence of corresponding 3D ground truth between the phantom inputs and the real target domain images. Metrics like SSIM or PSNR on generated pairs would assess image quality but not necessarily binocular consistency. No stereo-specific baseline was included as the goal was to compare against standard image-to-image translation methods. We will revise the abstract and discussion sections to better contextualize the evaluation and include any feasible quantitative analyses in the revised version. revision: partial

Circularity Check

0 steps flagged

No significant circularity; central claim rests on external human evaluation

full rationale

The paper describes a cross-domain cGAN for stereo-consistent image synthesis and supports its claim of improved depth perception solely via preference ratings from six independent human evaluators on 90 image pairs. No equations, fitted parameters, self-citations, or uniqueness theorems appear in the abstract or description that would reduce any prediction or result to a tautology by construction. The evaluation is performed by external raters on a 3D monitor and is therefore independent of the model's internal quantities, satisfying the criteria for a self-contained, non-circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no equations, training details, or explicit assumptions, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5807 in / 1132 out tokens · 39974 ms · 2026-05-25T16:50:25.423504+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages · 2 internal anchors

[1]

Improving surgi- cal training phantoms by hyperrealism: Deep unpaired image-to-image translation from real surgeries,

S. Engelhardt, R. De Simone, P. M. Full, M. Karck, and I. Wolf, “Improving surgi- cal training phantoms by hyperrealism: Deep unpaired image-to-image translation from real surgeries,” in Medical Image Computing and Computer Assisted Inter- vention – MICCAI 2018 , 2018, pp. 747–755

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I. Luengo, E. Flouty, P. Giataganas, P. Wisanuvej, J. Nehme, and D. Stoyanov, “Surreal: enhancing surgical simulation realism using style transfer,” inBritish Ma- chine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018 , 2018, p. 116

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Unpaired image-to-image trans- lation using cycle-consistent adversarial networks,

J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image trans- lation using cycle-consistent adversarial networks,” in 2017 IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 2242–2251

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D. Chen, L. Yuan, J. Liao, N. Yu, and G. Hua, “Stereoscopic neural style transfer,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018, pp. 6654–6663

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work page internal anchor Pith review Pith/arXiv arXiv 2014
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Image-to-Image Translation with Conditional Adversarial Networks

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” arXiv:1611.07004, Nov. 2016

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Z. Yi, H. Zhang, P. Tan, and M. Gong, “DualGAN: Unsupervised Dual Learning for Image-To-Image Translation,” inThe IEEE International Conference on Computer Vision (ICCV) , Oct 2017, pp. 2868–2876

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Flex- ible and Comprehensive Patient-Speciﬁc Mitral Valve Silicone Models with Chor- dae Tendinae Made From 3D-Printable Molds,

S. Engelhardt, S. Sauerzapf, B. Preim, M. Karck, I. Wolf, and R. De Simone, “Flex- ible and Comprehensive Patient-Speciﬁc Mitral Valve Silicone Models with Chor- dae Tendinae Made From 3D-Printable Molds,”International Journal of Computer Assisted Radiology and Surgery (IPCAI Special Issue) , vol. 14, no. 7, 2019

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Repli- cated mitral valve models from real patients oﬀer training opportunities for mini- mally invasive mitral valve repair,

S. Engelhardt, S. Sauerzapf, A. Bri, M. Karck, I. Wolf, and R. De Simone, “Repli- cated mitral valve models from real patients oﬀer training opportunities for mini- mally invasive mitral valve repair,” Interact Cardiovasc Thorac Surg. , 2019

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Improving surgi- cal training phantoms by hyperrealism: Deep unpaired image-to-image translation from real surgeries,

S. Engelhardt, R. De Simone, P. M. Full, M. Karck, and I. Wolf, “Improving surgi- cal training phantoms by hyperrealism: Deep unpaired image-to-image translation from real surgeries,” in Medical Image Computing and Computer Assisted Inter- vention – MICCAI 2018 , 2018, pp. 747–755

work page 2018

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A taxonomy of mixed reality visual displays,

P. Milgram and F. Kishino, “A taxonomy of mixed reality visual displays,” IEICE Trans Inf Syst , vol. 77, no. 12, pp. 1321–1329, 1994

work page 1994

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Surreal: enhancing surgical simulation realism using style transfer,

I. Luengo, E. Flouty, P. Giataganas, P. Wisanuvej, J. Nehme, and D. Stoyanov, “Surreal: enhancing surgical simulation realism using style transfer,” inBritish Ma- chine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018 , 2018, p. 116

work page 2018

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Unpaired image-to-image trans- lation using cycle-consistent adversarial networks,

J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image trans- lation using cycle-consistent adversarial networks,” in 2017 IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 2242–2251

work page 2017

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Stereoscopic neural style transfer,

D. Chen, L. Yuan, J. Liao, N. Yu, and G. Hua, “Stereoscopic neural style transfer,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018, pp. 6654–6663

work page 2018

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Conditional Generative Adversarial Nets

M. Mirza and S. Osindero, “Conditional Generative Adversarial Nets,” arXiv:1411.1784, Nov. 2014

work page internal anchor Pith review Pith/arXiv arXiv 2014

[7] [7]

Image-to-Image Translation with Conditional Adversarial Networks

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” arXiv:1611.07004, Nov. 2016

work page internal anchor Pith review Pith/arXiv arXiv 2016

[8] [8]

DualGAN: Unsupervised Dual Learning for Image-To-Image Translation,

Z. Yi, H. Zhang, P. Tan, and M. Gong, “DualGAN: Unsupervised Dual Learning for Image-To-Image Translation,” inThe IEEE International Conference on Computer Vision (ICCV) , Oct 2017, pp. 2868–2876

work page 2017

[9] [9]

Flex- ible and Comprehensive Patient-Speciﬁc Mitral Valve Silicone Models with Chor- dae Tendinae Made From 3D-Printable Molds,

S. Engelhardt, S. Sauerzapf, B. Preim, M. Karck, I. Wolf, and R. De Simone, “Flex- ible and Comprehensive Patient-Speciﬁc Mitral Valve Silicone Models with Chor- dae Tendinae Made From 3D-Printable Molds,”International Journal of Computer Assisted Radiology and Surgery (IPCAI Special Issue) , vol. 14, no. 7, 2019

work page 2019

[10] [10]

Repli- cated mitral valve models from real patients oﬀer training opportunities for mini- mally invasive mitral valve repair,

S. Engelhardt, S. Sauerzapf, A. Bri, M. Karck, I. Wolf, and R. De Simone, “Repli- cated mitral valve models from real patients oﬀer training opportunities for mini- mally invasive mitral valve repair,” Interact Cardiovasc Thorac Surg. , 2019

work page 2019