SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis

Bing Yang; Huazhu Fu; Jiang Liu; Jun Cheng; Mengjie Guo; Shenghua Gao; Tianyang Zhang; Yitian Zhao; Yuting Xiao; Zaiwang Gu

REVIEW 5 major objections 5 minor 19 references

SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis

T0 review · 5 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Decomposing medical image generation into a sketching step and a color-rendering step preserves fine anatomical structures, and this paper reports that the resulting unconditional GAN outperforms four comparison GANs across four…

desk verdict SkrGAN is a plausible two-stage sketch-then-render generator for medical images, with a useful data-augmentation experiment, but the equations as printed don't train the two stages together; that has to be fixed before the central claim is trustworthy. read the letter →

arxiv 1908.04346 v1 pith:A66JEZTD submitted 2019-08-06 cs.CV eess.IV

Tianyang Zhang , Huazhu Fu , Yitian Zhao , Jun Cheng , Mengjie Guo , Zaiwang Gu , Bing Yang , Yuting Xiao

show 2 more authors

Shenghua Gao Jiang Liu

This is my paper · ORCID

classification cs.CVeess.IV

keywords medicalimagesynthesisgenerativeadversarialnetworkssketchpriorunconditionalgenerationdataaugmentationretinalfundusvesselsegmentationstructurepreservation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper proposes SkrGAN, an unconditional generative adversarial network that splits medical image synthesis into a sketching stage and a color-rendering stage, following the order in which a human painter works. The sketch guidance module turns random noise into a structural sketch, and the color render mapping embeds that sketch into a full color image, so fine foreground structures such as retinal vessels, ribs, and bone edges are not left to the image discriminator alone. The authors report lower Fréchet Inception Distance, lower Sliced Wasserstein Distance, and higher MS-SSIM than four comparison GANs on retinal color fundus, chest X-ray, lung CT, and brain MRI. They further report that pretraining a U-net on 2,000 synthetic retinal pairs, using generated sketches as labels, raises vessel-segmentation sensitivity on DRIVE from 0.778 to 0.846. If the claim is right, SkrGAN is a general-purpose, label-free data-augmentation method for medical imaging.

What carries the argument

The load-bearing mechanism is a sketch prior enforced by two coupled modules. The sketch guidance module $G_S$ maps random noise to a structural sketch and answers to a sketch discriminator $D_S$ that compares against Sobel-and-morphology sketch drafts of real images; the color render mapping $G_P$, built on a U-net, embeds the sketch representation and produces the final color image under an image discriminator $D_P$ plus an $\ell_1$ reconstruction term. The two objectives are optimized jointly, and the sketch stage grows progressively in resolution. This isolates the anatomical geometry in a low-dimensional sketch space before the model attempts to match texture and background appearance.

What would settle it

Generate one fixed noise vector through SkrGAN, then compare the generated sketch with the Sobel/morphological sketch of the rendered color image; if thin vessels or bone edges diverge, the sketch prior is not preserving the structures claimed. A second check is to train with random line drawings of similar density in place of the real sketch drafts: if FID and segmentation scores remain unchanged, the specific sketch representation is doing none of the work.

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Extended reading notes

Core claim

SkrGAN's central claim is that an unconditional GAN can generate medical images with anatomically faithful foreground structures if the generator first commits to a structural sketch and only then renders color. The sketch guidance module $G_S$ is trained against a sketch discriminator $D_S$ on sketch drafts extracted from real images by Sobel edge detection, Gaussian low-pass filtering, and morphological opening/closing; the color render mapping $G_P$, a U-net, is trained against an image discriminator $D_P$ and an $\ell_1$ reconstruction loss. The full objective joins the sketch-guidance loss with the color-rendering loss, with progressive growing used in the sketch stage. On the paper's experiments, this architecture performs better than DCGAN, ACGAN, WGAN, and PGGAN on FID, SWD, and MS-SSIM across four modalities, and its synthetic images raise U-net vessel-segmentation sensitivity on DRIVE from 0.778 to 0.846. The intended contribution is a general-purpose structure-preserving generator and data-augmentation tool that needs neither paired data nor manual labels.

Load-bearing premise

The paper's results depend on the hand-made sketch representation (Sobel edges, smoothing, and morphological cleaning) capturing exactly the anatomical structures that matter; if that sketch drops thin vessels or blurs bone boundaries, both the image-quality gains and the segmentation improvement would shrink.

Editorial extensions

If this is right

On four modalities (retinal color fundus, chest X-ray, lung CT, brain MRI), SkrGAN reports lower FID and lower Sliced Wasserstein Distance, and higher MS-SSIM, than DCGAN, ACGAN, WGAN, and PGGAN.
The generator requires no paired data, labels, or depth maps, because the sketch prior is derived automatically from training images.
The synthetic images can be used as data augmentation: on DRIVE, pretraining a U-net with SkrGAN pairs raises sensitivity from 0.778 to 0.846, with accuracy and AUC also increasing.
The same architecture transfers across modalities without per-modality redesign, which makes it a candidate general-purpose augmentation tool when training sets are small.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the sketch representation is probably interchangeable; if so, SkrGAN should transfer to ultrasound, OCT, or dermoscopy by swapping the sketch-preparation recipe, a test the paper does not run.
Editorial inference: the gains could come mainly from constraining the image discriminator's input space rather than from the specific Sobel representation; this could be tested by replacing real sketch drafts with random line drawings of matched density.
Editorial inference: because the generated sketches double as labels, SkrGAN is a source of weakly labeled training data; a natural extension is to measure how segmentation accuracy scales beyond 2,000 synthetic pairs.
Editorial inference: the unconditional setting leaves room for a conditional extension that controls anatomy or pathology class, which would help class-imbalanced medical datasets; the paper does not explore this.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The genuinely new piece is the use of a hand-crafted edge-based sketch prior (Sobel plus Gaussian plus morphological opening/closing) inside an unconditional medical-image GAN. The split into a sketch generator and a sketch-to-color renderer has precedent in S2-GAN's style/structure decomposition, so this is an incremental architectural contribution, but a reasonable one. The experiments cover four modalities (fundus, X-ray, CT, MRI) with FID, SWD, MS-SSIM against DCGAN, ACGAN, WGAN, PGGAN, and a DRIVE vessel-segmentation augmentation test. The sensitivity gain from 0.778 to 0.846 with U-net pretraining is a practically interesting result, assuming it is real.

Now the soft spots. The loss functions in Eqs (1) and (2) are printed inverted: they would train the discriminators to call fake images real and real images fake. That is likely a sign typo, but it must be corrected. More seriously, even after fixing that, Eq (3) is a sum of two independent training problems. The sketch generator GS is trained only against the sketch discriminator DS; the renderer GP is trained only on real sketch-image pairs (y,x). There is no term involving GP(GS(z)). So the objective never couples the two modules, and at inference GP receives input from a distribution (generated sketches) that it never saw during training. The described 'sketch prior constraint to guide medical image generation' is not realized by the equations as printed. If the actual code includes a loss on the full generator, that term must be reported; without it, this is a two-stage pipeline, not an end-to-end sketch-guided GAN. This is a load-bearing issue, not cosmetic.

The quantitative reporting has smaller problems: mean metrics over 100 images with no error bars or significance tests; MS-SSIM on unpaired generation is ambiguous; Table 1 appears to have copy-paste errors (brain MRI FID values for DCGAN and ACGAN are identical to lung CT). Table 2 gives one pretraining run with no variance. None of this falsifies the central result, but it makes the effect size hard to judge.

Citation pattern is fine; self-citations are background.

Bottom line: this paper deserves referee time, but with major revision. The authors need to provide the real training objective, correct the sign flips, add error bars or significance tests, fix Table 1, clarify MS-SSIM, and ideally ablate the renderer trained on generated versus real sketches. The audience is medical-imaging GAN researchers and people using synthetic data for segmentation. I wouldn't cite it in its current form.

Referee Report

5 major / 5 minor

Summary. The paper proposes SkrGAN, an unconditional GAN for medical image synthesis that decomposes generation into a sketch guidance module (GS) and a color render mapping module (GP). Sketch drafts are extracted from real images using Sobel edge detection, Gaussian filtering, and morphological operations. GS is trained to generate sketches from noise under a sketch discriminator DS; GP is trained to translate sketches into color images under a color discriminator DP and an L1 loss. The paper evaluates the method on retinal fundus, chest X-ray, lung CT, and brain MRI datasets using MS-SSIM, SWD, and FID, and demonstrates a vessel-segmentation data-augmentation experiment on DRIVE. The abstract claims state-of-the-art synthesis and improved segmentation through SkrGAN-generated images.

Significance. If the claimed results hold, the paper would make a useful contribution by showing that an explicit structural sketch prior can improve unconditional medical image generation and provide a data-augmentation signal for segmentation. The application to four modalities and the DRIVE segmentation transfer experiment are appropriate tests, and the use of established metrics (FID, SWD, MS-SSIM) is commendable. However, the significance is currently undercut by formal problems in the stated objectives: the equations as printed do not implement the claimed sketch-guided generator, and the reported quantitative comparisons lack error bars or significance tests. The central mechanism must be correctly defined and verified before the empirical claims can be assessed.

major comments (5)

[Sec. 2.2, Eq. (1)] The adversarial objective for the sketch guidance module is printed with inverted roles: Ls = Ez[log(DS(GS(z⊙l)))] + Ex[log(1−DS(y))]. Under the standard convention that DS outputs the probability of a real input, this trains DS to label generated sketches as real and real sketches as fake, and trains GS to move its output toward the fake label. If this is a typo, the correct form must be given, because the subsequent derivation and the interpretation of the training dynamics depend on it.
[Sec. 2.3, Eq. (2)] The color render mapping objective has the same inversion: Ladv writes Ez,y[log(DP(GP(y),y))] for the generated pair and E(x,y)[log(1−DP(x,y))] for the real pair. As written, DP is rewarded for calling translated sketches real and original image-sketch pairs fake, which is the opposite of the standard conditional GAN loss. The authors should correct the sign convention or state explicitly if a nonstandard label convention is intended; as printed, the loss is not a valid adversarial objective.
[Secs. 2.2–2.3, Eqs. (1)–(3)] The total objective in Eq. (3) does not contain any term that evaluates GP on the output of GS: no loss depends on GP(GS(z)). Equation (2) trains GP on pairs of real sketches y and real images x, while Eq. (1) trains GS alone. Therefore the gradients with respect to GS are independent of GP, and the color renderer is never trained on generated sketches, even though Fig. 2 and the inference procedure feed GS(z) into GP. This is not a cosmetic issue: the claimed sketch-prior constraint guiding final image generation is not realized by the equations as written. The reported FID/SWD improvements and the segmentation gain could, in principle, be produced by the supervised sketch-to-image translator alone, with the unconditional sketch generator playing no role in final image quality. The authors must report the actual objective used in training; if GP(GS(z)) is indeed trained, that term must be added to Eq. (3), and if it is not, the central claim of the paper needs to be substantially revised.
[Sec. 3, Table 1] The quantitative comparison reports mean SWD, MS-SSIM, and FID computed from 100 generated images per method, with no variance, confidence intervals, or significance tests across independent training runs. FID and SWD are notoriously noisy at this sample size, and for some entries the gap to the runner-up is small (e.g., brain MRI FID of 27.51 versus 33.76 for PGGAN). The authors should report standard deviations over repeated generation/training runs or provide a statistical test to support the claim that SkrGAN is consistently better than the baselines.
[Secs. 2.1 and 3, vessel segmentation experiment] The paper provides no quantitative validation that the handcrafted sketch draft representation preserves the anatomical structures that matter (small vessels, disc/cup boundaries, bone edges). This is load-bearing because the segmentation pretraining uses the generated sketches as labels: if the sketch extractor drops small vessels, the reported +8.78% sensitivity improvement may reflect a denoised or simplified label signal rather than a genuinely better structural prior. The authors should compare their sketch extraction against the full images at the relevant resolutions and report, for example, vessel detection recall on the DRIVE ground truth, or an ablation that varies the sketch-generation parameters.

minor comments (5)

[Throughout] There are several typographical errors that should be corrected: 'Freshet Inception Distance' should be 'Fréchet Inception Distance', 'comparsions' should be 'comparisons', and Table 2 contains 'whithout' for 'without'. The PGGAN reference also contains 'et. al.o'.
[Sec. 3, datasets and protocol] The description of the evaluation protocol is incomplete: it is not stated how the real-image set for FID/SWD is defined, whether all real images are used, and whether the 100 generated images are produced after a fixed number of training iterations or after convergence. These details affect reproducibility.
[Eq. (3)] The notation 'arg[ min_GS max_DS Ls + min_GP max_DP (Ladv+L1) ]' is nonstandard and mixes the minimization and maximization arguments with the outer arg expression; this should be rewritten as separate min-max problems or as a single combined objective with clearly scoped optimization variables.
[Sec. 2.2] The definition DS = D_S^(n)···D_S^(0) and GS = G_S^(n)···G_S^(0) is not explained with enough precision; it would be clearer to state that the generator and discriminator are progressive-growing networks following PGGAN, rather than introducing notation that suggests a composition of separate networks.
[Sec. 3, hyperparameters] The statement that 'a small change of λ does not affect much the performance' is not supported by an ablation or sensitivity analysis; adding a short sensitivity experiment would strengthen the claim and the reproducibility of the method.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SkrGAN's evaluation is benchmarked externally and self-citations are background only.

full rationale

The paper's central derivation is not circular. The sketch guidance module GS is trained with a sketch discriminator against sketch drafts extracted from real images by Sobel edge detection, Gaussian filtering, and morphological operations (Sec. 2.1-2.2). The color render mapping GP is trained on real sketch-image pairs using an adversarial loss and an L1 reconstruction loss (Eq. 2), and the final objective in Eq. 3 is the sum of these two independent training losses. The reported image-quality results are measured against external baselines (DCGAN, ACGAN, WGAN, PGGAN) using FID, SWD, and MS-SSIM on public datasets, so no fitted parameter is renamed as a prediction. The segmentation experiment pretrains U-net on synthetic images with generated sketches as labels and then fine-tunes on the external DRIVE dataset; this is an independent empirical test rather than a conclusion contained in the training objective. The only self-citations, references [3] and [5], appear as generic background for deep learning in medical imaging and are not load-bearing. The skeptic's observation that Eqs. (1)-(3) never train GP on GS(z) is a possible architectural coherence or correctness concern, not a circularity: it does not show that any output is defined in terms of an input or that a result is forced by construction. The paper therefore receives a circularity score of 0.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The paper contributes an architecture, not a derivation; its central claim rests on a modeling assumption (sketch prior quality) and on hyperparameters chosen by hand. No new physical entities are introduced.

free parameters (6)

lambda (L1 loss weight) = 100
Set by experience in Sec 3; the paper says a small change does not affect performance much.
learning rate for GS and DS = 0.001
Chosen for sketch guidance module training; reported in Sec 3.
learning rate for GP and DP = 0.0002
Chosen for color render mapping training; reported in Sec 3.
batch size = 16
Reported in Sec 3.
number of generated images for evaluation = 100
Evaluation computed on 100 generated images per method; small sample.
number of synthetic images for pretraining = 2000
Used in vessel segmentation augmentation experiment; arbitrary.

assumptions (3)

domain assumption The extracted sketch drafts preserve the structural information needed for generation and downstream segmentation.
Sec 2.1 assumes Sobel + Gaussian blur + morph operations retain vessel/bone structure; no validation of sketch fidelity.
standard math The PGGAN backbone and U-net backbone are suitable for the two modules.
Sec 2.2-2.3 borrow architectures from prior work without further justification.
ad hoc to paper The adversarial objectives in Eqs. (1) and (2) are standard GAN losses up to label convention.
As printed they are inverted; the method depends on the reader interpreting them as the usual real/fake discriminator objectives.

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Cite this review

Pith. "Pith review of SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis." pith.science (2026). https://pith.science/paper/A66JEZTD

@misc{pith2026190804346,
  author       = {Pith},
  title        = {Pith review of: SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A66JEZTD}},
  note         = {Machine review of arXiv:1908.04346}
}

read the original abstract

Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may contain diagnostic indicators for medical image analysis. Inspired by human painting procedure, which is composed of stroking and color rendering steps, we propose a Sketching-rendering Unconditional Generative Adversarial Network (SkrGAN) to introduce a sketch prior constraint to guide the medical image generation. In our SkrGAN, a sketch guidance module is utilized to generate a high quality structural sketch from random noise, then a color render mapping is used to embed the sketch-based representations and resemble the background appearances. Experimental results show that the proposed SkrGAN achieves the state-of-the-art results in synthesizing images for various image modalities, including retinal color fundus, X-Ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In addition, we also show that the performances of medical image segmentation method have been improved by using our synthesized images as data augmentation.

Figures

Figures reproduced from arXiv: 1908.04346 by the authors.

**Figure 1.** Synthesized retinal images by PGGAN [7], DCGAN [14], ACGAN [11] and our SkrGAN. Compared with these methods, our method performs better in retaining structural details, e.g., blood vessels, disc and cup regions, as indicated by green arrows. is easy to collect data in conventional computer vision tasks, it is often difficult to obtain sufficient high quality data in medical imaging area. Recently, Generative Advers… view at source ↗

**Figure 2.** Illustration of our SkrGAN structure, which can generate medical images from the input noises. The sketch guidance module GS (blue block) obtains the representations based on sketch structure discriminator DS. The color render mapping GP (green block) embeds the sketch representations to generate the final color image with a discriminator DP . Moreover, We also extract a sketch draft dataset (bottom) for training … view at source ↗

**Figure 3.** These images are generated by different GANs: from left to right are results by: (a) PGGAN [7] , (b) WGAN [1] , (c) DCGAN [14] , (d) ACGAN [11] and (e) Our SkrGAN . The synthetic sketches generated from random noise are shown in the figure (f). From top to bottom, we show results from: CT, X-ray, Retina color fundus and MRI. The green arrows illustrate the structural distortions in the generated images. (More visual… view at source ↗

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Reference graph

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Reviewed August 14, 2026 · model on record in the stance chip above.

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