Pith. sign in

REVIEW 3 major objections 5 minor 47 references

This paper reports the first 3D virtual staining of X-ray histology: a modified CycleGAN turns greyscale SR-microCT slices of bone implants into realistic toluidine-blue-stained histology, and the trained network can color whole volumetric

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

A modified CycleGAN translates synchrotron micro-CT slices of bone implants into realistic virtual toluidine-blue histology, outperforming Pix2Pix and standard CycleGAN baselines.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection First deep-learning virtual staining for X-ray histology, honestly argued and useful, but the headline 3D generalization claim rests on a demo volume that contributed a training pair—needs a leave-one-volume-out check. the 3 major comments →

arxiv 2509.09235 v1 pith:KXNY3QZU submitted 2025-09-11 eess.IV cs.AIcs.CVphysics.comp-phq-bio.QM

Virtual staining for 3D X-ray histology of bone implants

Sarah C. Irvine , Christian Lucas , Diana Kr\"uger , Bianca Guedert , Julian Moosmann , Berit Zeller-Plumhoff This is my paper
classification eess.IV cs.AIcs.CVphysics.comp-phq-bio.QM
keywords virtual staining3D X-ray histologyimage-to-image translationCycleGANmicro-CTbone implantstoluidine bluedeep learning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The authors claim that virtual staining, previously limited to optical microscopy, can be extended to 3D X-ray histology. Using more than 50 co-registered pairs of synchrotron micro-CT and toluidine-blue histology from bone-implant samples, they train a CycleGAN modified with pixelwise supervision and greyscale-consistency losses to translate greyscale CT slices into stained-looking colour images. They report that this model outperforms Pix2Pix and standard CycleGAN on SSIM, PSNR, and LPIPS, and that applying it slice-by-slice to a CT volume yields a virtually stained 3D dataset. A sympathetic reader would care because it offers a non-destructive, label-free route to chemically informative tissue characterisation without physical sectioning or staining.

Core claim

The central claim is that paired X-ray and histology data can be used to learn a cross-modality mapping that generates histology-equivalent colour from greyscale micro-CT, and that this mapping works in 3D. The paper's specific discovery is that adding two loss terms to CycleGAN — an l1 pixelwise supervision term that penalises differences between generated and real paired images, and a greyscale-consistency term that forces the reverse-generated CT to be single-channel — stabilises training and produces outputs that preserve structures like bone pores, lacunae, and new woven bone. The trained forward generator colours a full CT volume slice by slice, and the paper reports that the generated

What carries the argument

The central object is a modified CycleGAN with two generators (CT→histology and histology→CT), two PatchGAN discriminators, cycle-consistency loss, identity loss, and two added terms: pixelwise l1 supervision for paired inputs and a greyscale-consistency loss on the reverse-generated CT. Sample correspondence masks exclude regions where CT and histology are known to mismatch, and global 2D-3D registration (a rigid transformation of the CT volume plus an affine correction of the histology image) provides the alignment that makes pixelwise supervision meaningful. Patch-based training with on-the-fly augmentation and overlapping patch-based inference produce whole-slide outputs.

Load-bearing premise

The result depends on the global 2D-3D registration between histology and micro-CT being accurate enough that pixelwise l1 supervision teaches the correct correspondence; the paper itself notes that soft tissue regions shift and patches are not aligned to every pixel.

What would settle it

If the global registration is the limiting factor, then re-registering the same pairs with a per-patch non-rigid method and retraining should improve SSIM/LPIPS; if it does not, the reported advantage may partly reflect tolerance to misalignment rather than true fidelity. A direct check would be to manually correct a set of validation patches and see whether predictions move toward the corrected ground truth, or to degrade alignment artificially and show that performance falls as expected.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Once trained, the model can be applied to any micro-CT volume of similar samples to generate virtually stained slices without additional histology, enabling 3D visual interpretation of whole specimens.
  • The reported quantitative advantage over Pix2Pix and standard CycleGAN (e.g., median SSIM of about 0.59–0.63 versus 0.21–0.24 on test/validation) suggests that combining paired supervision with cycle consistency is a useful recipe for partially misaligned cross-modality medical images.
  • Preliminary results on a smaller H&E-stained dataset indicate the approach may generalize across staining protocols, not just toluidine blue.
  • The 3D output can highlight biologically relevant features such as new woven bone adjacent to implant degradation layers, which are not immediately obvious in the raw CT images.

Where Pith is reading between the lines

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

  • If registration quality is the bottleneck, a per-patch or non-rigid registration refinement before training could improve pixelwise supervision and may push SSIM/LPIPS further; the paper's own note about soft-tissue shifting suggests a concrete experiment.
  • The resolution analysis places generated histology between CT and real histology; an extension would be a super-resolution version that upscales CT to match original histology resolution, potentially improving fidelity.
  • The model's difficulty with degradation layers and screw materials suggests that a multi-class conditioning input (e.g., material label) could reduce ambiguity, though the authors report one such attempt caused blurring; testing alternative conditioning strategies is a natural next step.
  • A testable extension is to evaluate whether the virtual stain preserves quantitative measures such as bone-implant contact fraction or degradation-layer thickness computed from generated volumes against histology ground truth, which would directly test clinical utility.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents a modified CycleGAN for virtual staining of synchrotron micro-CT slices into toluidine-blue histology of bone-implant samples. The model adds pixelwise L1 supervision and a greyscale-consistency loss to standard CycleGAN, trains on ~50 co-registered WSI pairs with on-the-fly augmentation and sample-correspondence masks, and produces whole-slide outputs via patch-based inference. Quantitative comparison with Pix2Pix and standard CycleGAN is reported on SSIM, LPIPS, and PSNR for training, validation, and a held-out test set of 3 WSI pairs. The authors also demonstrate generation of a 3D virtually stained volume and claim this is the first 3D virtual staining for X-ray histology.

Significance. If the 3D claim is supported, this is a valuable extension of virtual staining to a new imaging domain with practical relevance for non-destructive bone-implant evaluation. The paper is honest about colour mismatches, degradation-layer variability, and registration limitations, and it includes a secondary H&E-staining test that strengthens generality. The main strengths are the paired-data adaptation of CycleGAN, whole-slide inference, and the use of sample-correspondence masks. However, the central 3D demonstration is not held out, the quantitative comparison uses only 3 test images without significance testing, and registration error is acknowledged but not quantified. These gaps must be addressed before the headline claims are fully supported.

major comments (3)
  1. [§2.11, §3.3, Fig. 8] The 3D virtual-staining demonstration is not an out-of-sample test. Section 2.11 explicitly states that the CT stack used for the 3D volume had 'one training pair also produced from this dataset through the co-registration process.' The model was therefore trained on a slice from the same specimen it later stained in 3D. The bright-blue new-bone regions in Fig. 8 could reflect sample-specific memorization rather than a generalizable volumetric capability. The abstract's claim that 'once trained, the model can be applied to full CT volumes' needs support from a leave-one-volume-out retraining experiment on a specimen that contributed no training pairs, or the claim should be downgraded to an in-distribution demonstration.
  2. [§2.4, Table 1, Fig. 7] The quantitative comparison is based on only 3 held-out WSI pairs, with median values and box plots but no confidence intervals or significance tests. The repeated claim that the modified CycleGAN 'significantly outperforms' Pix2Pix and standard CycleGAN is not statistically supported. The authors should report per-test-pair scores and perform paired tests (e.g., Wilcoxon signed-rank) or provide patch-level bootstrap confidence intervals. Given the small test set, the text should also state the limited generalizability of the numerical comparison.
  3. [§2.3, §3.1, Fig. 4a] The pixelwise L1 supervision relies on global 2D–3D registration that is acknowledged to be imperfect for soft tissue. The manuscript states in §3.1 that 'patches are not perfectly aligned down to every pixel' and that soft bone tissues shift. This creates a correctness risk: if registration errors are substantial, the reported advantage over Pix2Pix may partly reflect tolerance to misalignment rather than true histology-fidelity. The authors should quantify registration error (e.g., landmark distances or Dice on rigid structures) and, ideally, evaluate whether local affine refinement changes the comparative results. At minimum, this uncertainty should be stated as a factor limiting the quantitative claims.
minor comments (5)
  1. [§2.6.1, Eq. (7)] The loss weights λcyc=6, λid=3, λpx=6, λgs=1 are given without sensitivity analysis. A short ablation table would clarify how each added term contributes to the final performance, especially λgs, which is described as identity-preserving for greyscale.
  2. [§2.11] The 3D Gaussian filter with sigma=1 pixel is an ad hoc simulation of resolution loss from registration interpolation. Its effect on the generated volume should be described more precisely, and the choice justified or varied.
  3. [Fig. 8] The 3D render lacks scale bars and voxel-depth information. Adding these would help the reader judge the slice-stack dimensions and the magnitude of the stripe artefacts in the YZ and XZ planes.
  4. [§3.1, Table 1] The 'standard CycleGAN' baseline is trained on paired data but only with the unpaired CycleGAN losses, and the authors add a second variant with inverted CT input. This is a reasonable ablation, but the naming is potentially confusing; clearly label it as 'CycleGAN with paired data but without supervision losses' throughout the text.
  5. [§4] The claim of 'first known demonstration' should be qualified by the in-sample nature of the 3D volume and the small test set. Also, no data or code availability statement is provided; sharing the registration pipeline and trained model would improve reproducibility.

Circularity Check

1 steps flagged

Core 2D metrics are held out, but the 3D demonstration is partially in-sample and the new-bone check is a self-consistency check.

specific steps
  1. fitted input called prediction [Section 2.11 (3D testing), Section 3.3 (3D qualitative results), Fig. 8]
    "These were untransformed slices, although one training pair was also produced from this dataset through the co-registration process outlined in Subsection 2.3."

    The 3D volume showcased in Fig. 8 is presented as a demonstration that the model can be applied to full CT volumes, but the same dataset supplied one co-registered training pair. The model was therefore fitted on a slice from this exact specimen before being used to 'virtually stain' the whole volume. The highlighted new-bone regions in the volume are not an out-of-sample prediction; they could reflect sample-specific memorization of the learned low-attenuation-to-bright-blue mapping rather than generalizable volumetric staining. This does not reduce the 2D benchmark results, which use 3 held-out WSI pairs, but it makes the 3D generalization claim only weakly supported by the presented evidence.

full rationale

The central 2D quantitative claim is based on 3 test WSI pairs that were kept separate and never involved in training (Section 2.4), so the SSIM/LPIPS/PSNR comparison against Pix2Pix and standard CycleGAN is a genuine held-out evaluation. The modified CycleGAN loss is a standard combination of adversarial, cycle, identity, pixelwise l1, and greyscale-consistency terms; none of these terms is defined in terms of the test outputs, so there is no equation-level circularity in the metric comparison. The 3D demonstration, however, is not fully out-of-sample: Section 2.11 explicitly states that one training pair was produced from the same CT stack used for the 3D volume. This is data leakage rather than strict derivation-equivalence, but it weakens the abstract's claim that 'once trained, the model can be applied to full CT volumes' as an independent validation. The new-bone check in Section 3.3 is also a self-consistency check: the model was trained to associate low CT attenuation with bright blue histology (new woven bone), so verifying that predicted blue regions have low greyscale values in the input CT merely recovers the training signal; it does not independently confirm biological accuracy. Self-citations to Krüger et al. (2022), Irvine et al. (2024), and related prior work are used for data provenance, registration methodology, and feature interpretation, not as a load-bearing uniqueness theorem or as a substitute for the reported experiments. Overall, the main metric results are self-contained; the 3D generalization claim is weakened by the in-sample demonstration and by the self-consistency validation, giving a low circularity score of 2.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 0 invented entities

The central claim rests on the assumption that co-registered microCT and histology pairs are aligned well enough for pixelwise supervision, that stain colour can be learned from X-ray attenuation, and that hyperparameters chosen on validation transfer to test data. No new physical entities are introduced.

free parameters (5)
  • lambda_cyc = 6
    Cycle consistency weight in Eq. 7, selected via validation for good results (Section 2.6.3).
  • lambda_id = 3
    Identity loss weight in Eq. 7 (Section 2.6.3).
  • lambda_px = 6
    Pixelwise supervision weight in Eq. 7 (Section 2.6.3).
  • lambda_gs = 1
    Greyscale consistency weight in Eq. 7 (Section 2.6.3).
  • lambda_l1 (Pix2Pix) = 20
    L1 weight in Eq. 10, selected after tuning (Section 2.6.3).
axioms (5)
  • domain assumption Co-registered microCT-histology pairs are globally aligned well enough for pixelwise l1 supervision, including in soft-tissue regions
    Section 2.3 describes a global 2D-3D registration; Section 3.1 and Figure 4a acknowledge that soft tissue patches shift and are not aligned down to every pixel.
  • domain assumption Histological stain colour is a learnable function of local X-ray attenuation patterns
    The image translation task assumes that structures visible in histology (bone, soft tissue, degradation layer, new bone) have consistent CT attenuation signatures across the 53 pairs, despite different beamtimes, energies, and cameras.
  • domain assumption Sample correspondence masks correctly identify regions where microCT and histology do not correspond
    Section 2.8 uses convex hulls plus semi-manual masks to exclude mismatched regions from all l1 loss terms; errors in these masks would propagate into training.
  • domain assumption The 40/10 train-validation split and the 3 held-out test pairs adequately represent the full data distribution
    Section 2.4 describes a random split with a representative Mg:Ti:PEEK ratio, but only 3 test pairs are used for the headline comparison.
  • standard math CycleGAN adversarial training converges and PatchGAN discriminators provide meaningful gradients
    Assumed from prior art (Zhu et al. 2017) and not re-derived in this paper.

reviewed 2026-08-04 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Virtual staining for 3D X-ray histology of bone implants." pith.science (2026). https://pith.science/paper/KXNY3QZU

@misc{pith2026250909235,
  author       = {Pith},
  title        = {Pith review of: Virtual staining for 3D X-ray histology of bone implants},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KXNY3QZU}},
  note         = {Machine review of arXiv:2509.09235}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Three-dimensional X-ray histology techniques offer a non-invasive alternative to conventional 2D histology, enabling volumetric imaging of biological tissues without the need for physical sectioning or chemical staining. However, the inherent greyscale image contrast of X-ray tomography limits its biochemical specificity compared to traditional histological stains. Within digital pathology, deep learning-based virtual staining has demonstrated utility in simulating stained appearances from label-free optical images. In this study, we extend virtual staining to the X-ray domain by applying cross-modality image translation to generate artificially stained slices from synchrotron-radiation-based micro-CT scans. Using over 50 co-registered image pairs of micro-CT and toluidine blue-stained histology from bone-implant samples, we trained a modified CycleGAN network tailored for limited paired data. Whole slide histology images were downsampled to match the voxel size of the CT data, with on-the-fly data augmentation for patch-based training. The model incorporates pixelwise supervision and greyscale consistency terms, producing histologically realistic colour outputs while preserving high-resolution structural detail. Our method outperformed Pix2Pix and standard CycleGAN baselines across SSIM, PSNR, and LPIPS metrics. Once trained, the model can be applied to full CT volumes to generate virtually stained 3D datasets, enhancing interpretability without additional sample preparation. While features such as new bone formation were able to be reproduced, some variability in the depiction of implant degradation layers highlights the need for further training data and refinement. This work introduces virtual staining to 3D X-ray imaging and offers a scalable route for chemically informative, label-free tissue characterisation in biomedical research.

Figures

Figures reproduced from arXiv: 2509.09235 by Berit Zeller-Plumhoff, Bianca Guedert, Christian Lucas, Diana Kr\"uger, Julian Moosmann, Sarah C. Irvine.

Figure 1
Figure 1. Figure 1: An illustrative example of our X-ray-histology application, with (real) paired toluidine-blue stained histological (a) and X-ray tomographic (b) slice images of a Mg-10Gd screw implant in bone extracted after 8 weeks of healing time in vivo. In the histology, the green line marks the interface between the residual alloy and the degradation layer. In the tomographic image, the red line indicates the boundar… view at source ↗
Figure 2
Figure 2. Figure 2: Schematic of the methodology for 3D virtual staining in X-ray histology of bone implants, using a paired CycleGAN network. Following the sample preparation of the bone-implant block, the methodology timeline is illustrated schematically in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The modified CycleGAN for paired data, with additional pixelwise supervision loss terms that directly penalise differences between the generator output and the target image for each domain. Not shown: an additional greyscale loss term applicable to the generated µCT data only. 2.6.1 CycleGAN and its adaptation for paired data Our chosen model is based on the CycleGAN framework and adapted for paired data. … view at source ↗
Figure 4
Figure 4. Figure 4: Examples of our modified CycleGAN model training results: (a) direct training patch input/outputs (256 x 256 pixels); and (b) example WSI result generated through overlapping patch-based inference, from 1 WSI sample pair included in training (958 × 1000 pixels). The displayed µCT and histology images have been masked (replacing all background pixels with the mean value) with the included sample corresponde… view at source ↗
Figure 5
Figure 5. Figure 5: Example modified CycleGAN model results (with WSI output) for (a): training, (b) validation and (c) testing. Includes a 256 × 256 pixels region of interest (ROI) with accompanying RGB histograms and intensity profiles of real vs generated examples. Each profile is acquired horizontally across the midpoint of the ROI as indicated by the orange arrow. The scalebar in each WSI represents 500 µm. In (c), isola… view at source ↗
Figure 6
Figure 6. Figure 6: Inference results from each of the paired model variants tested, with WSI and 256 pixel ROI examples from validation and test data sets. ‘Mod. CycleGAN’ = the modified CycleGAN including the extra supervisory and greyscale loss terms, which is our chosen model. ‘Std. CycleGAN’ = the Standard CycleGAN model, which was trained from the same paired data sets, but without the extra loss terms. ‘Std. CycleGAN +… view at source ↗
Figure 7
Figure 7. Figure 7: Box plots for a comparison of the paired model variants tested, across the three metrics of Structural Similarity (SSIM) metric, Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS). applied with both normal input µCT and inverted µCT input). Measurement values for each of the metrics: SSIM, PSNR and LPIPS are shown, for each of training, validation and testing samples. … view at source ↗
Figure 8
Figure 8. Figure 8: Orthogonal views (a-c) and volume rendering with direct RGB mapping (d) of a generated 3D X-ray virtual histology dataset featuring a Mg-based degradable bone-implant sample. A real histology image acquired from the same bone-implant sample is shown for reference in the bottom-left inset of (a). Red arrows point to a region of new bone growth adjacent to the degradation layer, distinctly coloured in a brig… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

47 extracted references · 4 linked inside Pith

  1. [1]

    C., Filan, C

    Abraham, T., Costa, P. C., Filan, C. E., Robles, F., and Levenson, R. M. (2022). Mode-mapping qOBM microscopy to virtual hematoxylin and eosin ( H & E ) histology via deep learning. In Georges, M. P., Popescu, G., and Verrier, N., editors, Unconventional Optical Imaging III , page 58, Strasbourg, France. SPIE

    2022

  2. [2]

    Abraham, T. M. and Levenson, R. (2024). Current Landscape of Advanced Imaging Tools for Pathology Diagnostics . Modern Pathology , 37(4):100443

    2024

  3. [3]

    A., Wiart, M., Vande Velde, G., Tromba, G., and Dullin, C

    Albers, J., Pacilé, S., Markus, M. A., Wiart, M., Vande Velde, G., Tromba, G., and Dullin, C. (2018). X-ray- Based 3D Virtual Histology — Adding the Next Dimension to Histological Analysis . Molecular Imaging and Biology , 20(5):732--741

    2018

  4. [4]

    Bai, B., Yang, X., Li, Y., Zhang, Y., Pillar, N., and Ozcan, A. (2023). Deep learning-enabled virtual histological staining of biological samples. Light: Science & Applications , 12(1):57

    2023

  5. [5]

    M., Ćwieka, H., Krüger, D., Zeller-Plumhoff, B., Schlünzen, F., Willumeit-Römer, R., Moosmann, J., and Heuser, P

    Baltruschat, I. M., Ćwieka, H., Krüger, D., Zeller-Plumhoff, B., Schlünzen, F., Willumeit-Römer, R., Moosmann, J., and Heuser, P. (2021). Scaling the U -net: segmentation of biodegradable bone implants in high-resolution synchrotron radiation microtomograms. Scientific Reports , 11(1):24237

    2021

  6. [6]

    Chen, Z., Yu, W., Wong, I. H. M., and Wong, T. T. W. (2021). Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging. Biomedical Optics Express , 12(9):5920

    2021

  7. [7]

    Detlefsen, N., Borovec, J., Schock, J., Jha, A., Koker, T., Di Liello, L., Stancl, D., Quan, C., Grechkin, M., and Falcon, W. (2022). TorchMetrics - Measuring Reproducibility in PyTorch . Journal of Open Source Software , 7(70):4101

    2022

  8. [8]

    Donath, K. (1988). Preparation of histologic sections by the cutting-grinding technique for hard tissue and other material not suitable to be sectioned by routine methods. Equipment and methodical performance . Publisher: EXAKT-Kulzer-Publication

    1988

  9. [9]

    T., Riedmiller, M., and Brox, T

    Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T. (2014). Discriminative unsupervised feature learning with convolutional neural networks. Advances in neural information processing systems , 27

    2014

  10. [10]

    J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y

    Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems , 27

    2014

  11. [11]

    J., Liu, T., and Yang, X

    Harms, J., Lei, Y., Wang, T., Zhang, R., Zhou, J., Tang, X., Curran, W. J., Liu, T., and Yang, X. (2019). Paired cycle‐ GAN ‐based image correction for quantitative cone‐beam computed tomography. Medical Physics , 46(9):3998--4009

    2019

  12. [12]

    G., Khan, A., Ali, F., AlZubi, A

    Heng, Y., Yinghua, M., Khan, F. G., Khan, A., Ali, F., AlZubi, A. A., and Hui, Z. (2024). Survey: application and analysis of generative adversarial networks in medical images. Artificial Intelligence Review , 58(2):39

    2024

  13. [13]

    C., Lucas, C., Bootbool, M., Galli, S., Zeller-Plumhoff, B., and Moosmann, J

    Irvine, S. C., Lucas, C., Bootbool, M., Galli, S., Zeller-Plumhoff, B., and Moosmann, J. P. (2024). Multi-modal image registration and machine learning for the generation of 3D virtual histology of bone implants. In Müller, B. and Wang, G., editors, Developments in X - Ray Tomography XV , page 70, San Diego, United States. SPIE

    2024

  14. [14]

    M., Galli, S., Pröfrock, D., Will, O., Gerle, M., Damm, T., Sefa, S., He, W., MacRenaris, K., Soujon, M., Beckmann, F., Moosmann, J., O'Hallaran, T., Guillory, R

    Iskhakova, K., Cwieka, H., Meers, S., Helmholz, H., Davydok, A., Storm, M., Baltruschat, I. M., Galli, S., Pröfrock, D., Will, O., Gerle, M., Damm, T., Sefa, S., He, W., MacRenaris, K., Soujon, M., Beckmann, F., Moosmann, J., O'Hallaran, T., Guillory, R. J., Wieland, D. F., Zeller-Plumhoff, B., and Willumeit-Römer, R. (2024). Multi-modal investigation of ...

    2024

  15. [15]

    Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2018). Image-to- Image Translation with Conditional Adversarial Networks . arXiv:1611.07004 [cs]

    Pith/arXiv arXiv 2018

  16. [16]

    and Kida, S

    Kaji, S. and Kida, S. (2019). Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging. Radiological Physics and Technology , 12(3):235--248

    2019

  17. [17]

    Kang, L., Li, X., Zhang, Y., and Wong, T. T. (2022). Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining. Photoacoustics , 25:100308

    2022

  18. [18]

    E., and Müller, B

    Khimchenko, A., Deyhle, H., Schulz, G., Schweighauser, G., Hench, J., Chicherova, N., Bikis, C., Hieber, S. E., and Müller, B. (2016). Extending two-dimensional histology into the third dimension through conventional micro computed tomography. NeuroImage , 139:26--36

    2016

  19. [19]

    Koivukoski, S., Khan, U., Ruusuvuori, P., and Latonen, L. (2023). Unstained Tissue Imaging and Virtual Hematoxylin and Eosin Staining of Histologic Whole Slide Images . Laboratory Investigation , 103(5):100070

    2023

  20. [20]

    F., Peruzzi, N., Wiese, B., Heuser, P., Moosmann, J., Wennerberg, A., and Willumeit-Römer, R

    Krüger, D., Galli, S., Zeller-Plumhoff, B., Wieland, D. F., Peruzzi, N., Wiese, B., Heuser, P., Moosmann, J., Wennerberg, A., and Willumeit-Römer, R. (2022). High-resolution ex vivo analysis of the degradation and osseointegration of Mg - xGd implant screws in 3D . Bioactive Materials , 13:37--52

    2022

  21. [21]

    F., Moosmann, J., and Willumeit-Römer, R

    Krüger, D., Zeller-Plumhoff, B., Wiese, B., Yi, S., Zuber, M., Wieland, D. F., Moosmann, J., and Willumeit-Römer, R. (2021). Assessing the microstructure and in vitro degradation behavior of Mg - xGd screw implants using µ CT . Journal of Magnesium and Alloys , 9(6):2207--2222

    2021

  22. [22]

    Latonen, L., Koivukoski, S., Khan, U., and Ruusuvuori, P. (2024). Virtual staining for histology by deep learning. Trends in Biotechnology , 42(9):1177--1191

    2024

  23. [23]

    B., Curran, W

    Lei, Y., Harms, J., Wang, T., Liu, Y., Shu, H., Jani, A. B., Curran, W. J., Mao, H., Liu, T., and Yang, X. (2019). MRI ‐only based synthetic CT generation using dense cycle consistent generative adversarial networks. Medical Physics , 46(8):3565--3581

    2019

  24. [24]

    Linder-Norén, E. (2019). PyTorch implementations of Generative adversarial networks. Retrieved from: https://github. com/eriklindernoren/PyTorch-GAN

    2019

  25. [25]

    R., Elder, D

    Liu, S., Liu, K., Margolis, S., Wu, W., Knezevich, S. R., Elder, D. E., Eguchi, M. M., Elmore, J. G., and Shapiro, L. (2024). Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency . arXiv:2410.01072 [eess]

    Pith/arXiv arXiv 2024

  26. [26]

    A., Ferstl, S., Allner, S., Achterhold, K., Herzen, J., Pfeiffer, F., and Busse, M

    Müller, M., Kimm, M. A., Ferstl, S., Allner, S., Achterhold, K., Herzen, J., Pfeiffer, F., and Busse, M. (2018). Nucleus-specific X -ray stain for 3D virtual histology. Scientific Reports , 8(1):17855

    2018

  27. [27]

    P., Lidke, K

    Nieuwenhuizen, R. P., Lidke, K. A., Bates, M., Puig, D. L., Grünwald, D., Stallinga, S., and Rieger, B. (2013). Measuring image resolution in optical nanoscopy. Nature methods , 10(6):557--562. Publisher: Nature Publishing Group US New York

    2013

  28. [28]

    Peev, S., Parushev, I., and Yotsova, R. (2024). A Modified Protocol for Staining of Undecalcified Bone Samples Using Toluidine Blue — A Histological Study in Rabbit Models . Applied Sciences , 14(1):461

    2024

  29. [29]

    M., Busse, M., Mohr, H., Pellegata, N

    Petzold, L. M., Busse, M., Mohr, H., Pellegata, N. S., Pfeiffer, F., and Herzen, J. (2024). Development of a H & E multi-agent-staining method for laboratory- microCT applied to endocrine glands of MENX rats. In Müller, B. and Wang, G., editors, Developments in X - Ray Tomography XV , page 3, San Diego, United States. SPIE

    2024

  30. [30]

    Rieger, B., Droste, I., Gerritsma, F., Ten Brink, T., and Stallinga, S. (2024). Single image Fourier ring correlation. Optics Express , 32(12):21767

    2024

  31. [31]

    E., Chong, T., Sisk, A

    Rivenson, Y., Wang, H., Wei, Z., De Haan, K., Zhang, Y., Wu, Y., Günaydın, H., Zuckerman, J. E., Chong, T., Sisk, A. E., Westbrook, L. M., Wallace, W. D., and Ozcan, A. (2019). Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nature Biomedical Engineering , 3(6):466--477

    2019

  32. [32]

    A., and Müller, B

    Rodgers, G., Bikis, C., Janz, P., Tanner, C., Schulz, G., Thalmann, P., Haas, C. A., and Müller, B. (2023). 3D X -ray Histology for the Investigation of Temporal Lobe Epilepsy in a Mouse Model . Microscopy and Microanalysis , 29(5):1730--1745

    2023

  33. [33]

    L., Sammarra, M., Zobel, B

    Rofena, A., Guarrasi, V., Sarli, M., Piccolo, C. L., Sammarra, M., Zobel, B. B., and Soda, P. (2024). A deep learning approach for virtual contrast enhancement in Contrast Enhanced Spectral Mammography . Computerized Medical Imaging and Graphics , 116:102398

    2024

  34. [34]

    Ronneberger, O., Fischer, P., and Brox, T. (2015). U- Net : Convolutional Networks for Biomedical Image Segmentation . In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., editors, Medical Image Computing and Computer - Assisted Intervention – MICCAI 2015 , volume 9351, pages 234--241. Springer International Publishing, Cham. Series Title: Lectur...

    2015

  35. [35]

    B., Lindblad, J., Borgefors, G., and Franke Stenport, V

    Sarve, H., Johansson, C. B., Lindblad, J., Borgefors, G., and Franke Stenport, V. (2007). Quantification of Bone Remodeling in the Proximity of Implants . In Kropatsch, W. G., Kampel, M., and Hanbury, A., editors, Computer Analysis of Images and Patterns , volume 4673, pages 253--260. Springer Berlin Heidelberg, Berlin, Heidelberg. ISSN: 0302-9743, 1611-3...

    2007

  36. [36]

    F., Willumeit-Römer, R., and Zeller-Plumhoff, B

    Sefa, S., Espiritu, J., Ćwieka, H., Greving, I., Flenner, S., Will, O., Beuer, S., Wieland, D. F., Willumeit-Römer, R., and Zeller-Plumhoff, B. (2023). Multiscale morphological analysis of bone microarchitecture around Mg - 10Gd implants. Bioactive Materials , 30:154--168

    2023

  37. [37]

    Tripathy, S., Kannala, J., and Rahtu, E. (2018). Learning image-to-image translation using paired and unpaired training samples. arXiv:1805.03189 [cs]

    Pith/arXiv arXiv 2018

  38. [38]

    Töpperwien, M., Van Der Meer, F., Stadelmann, C., and Salditt, T. (2018). Three-dimensional virtual histology of human cerebellum by X -ray phase-contrast tomography. Proceedings of the National Academy of Sciences , 115(27):6940--6945

    2018

  39. [39]

    Ueda, Y., Niu, M., Shimazaki, R., Yamazaki, A., Seki, M., and Ishida, T. (2025). Deep Learning on Misaligned Dual - Energy Chest X -ray Images Using Paired Cycle - Consistent Generative Adversarial Networks . Journal of Imaging Informatics in Medicine

    2025

  40. [40]

    L., Nunez-Iglesias, J., Boulogne, F., Warner, J

    Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T. (2014). scikit-image: image processing in Python . PeerJ , 2:e453. Publisher: PeerJ Inc

    2014

  41. [41]

    Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing , 13(4):600--612

    2004

  42. [42]

    U., Beckmann, F., Hipp, A., Lottermoser, L., Khokhriakov, I., Lytaev, P., Dose, T., Burmester, H., Müller, M., and Schreyer, A

    Wilde, F., Ogurreck, M., Greving, I., Hammel, J. U., Beckmann, F., Hipp, A., Lottermoser, L., Khokhriakov, I., Lytaev, P., Dose, T., Burmester, H., Müller, M., and Schreyer, A. (2016). Micro- CT at the imaging beamline P05 at PETRA III . page 030035, New York, NY USA

    2016

  43. [43]

    Yi, X., Walia, E., and Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical Image Analysis , 58:101552

    2019

  44. [44]

    Zeller-Plumhoff, B., Tolnai, D., Wolff, M., Greving, I., Hort, N., and Willumeit-Römer, R. (2021). Utilizing Synchrotron Radiation for the Characterization of Biodegradable Magnesium Alloys — From Alloy Development to the Application as Implant Material . Advanced Engineering Materials , 23(11):2100197

    2021

  45. [45]

    A., Shechtman, E., and Wang, O

    Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O. (2018). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric . In 2018 IEEE / CVF Conference on Computer Vision and Pattern Recognition , pages 586--595, Salt Lake City, UT. IEEE

    2018

  46. [46]

    Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017). Unpaired Image -to- Image Translation Using Cycle - Consistent Adversarial Networks . In 2017 IEEE International Conference on Computer Vision ( ICCV ) , pages 2242--2251, Venice. IEEE

    2017

  47. [47]

    Zingman, I., Frayle, S., Tankoyeu, I., Sukhanov, S., and Heinemann, F. (2023). A comparative evaluation of image-to-image translation methods for stain transfer in histopathology. arXiv:2303.17009 [eess]

    Pith/arXiv arXiv 2023

This paper was first reviewed by deepseek-v4-flash on August 4, 2026.