Pith. sign in

REVIEW 5 major objections 7 minor 54 references

Swin-X2S: Reconstructing 3D Shape from 2D Biplanar X-ray with Swin Transformers

T0 review · 5 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Swin-X2S turns two orthogonal X-ray views into labeled 3D bone segmentation in one pass.

desk verdict Solid multi-anatomy DRR-to-3D reconstruction with honest limitations; clinical claims rest on untested real-X-ray generalization. read the letter →

arxiv 2501.05961 v1 pith:PJZODB6N submitted 2025-01-10 cs.CV eess.IV

Kuan Liu , Zongyuan Ying , Jie Jin , Dongyan Li , Ping Huang , Wenjian Wu , Zhe Chen , Jin Qi
show 3 more authors
Yong Lu Lianfu Deng Bo Chen
This is my paper · ORCID
classification cs.CVeess.IV
keywords 2D-to-3DreconstructionbiplanarX-raySwinTransformer3Dsegmentationandlabelingdigitallyreconstructedradiographsboneshapecross-attention54categories
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 introduces Swin-X2S, an end-to-end deep network that takes two uncropped orthogonal X-ray images (coronal and sagittal) and directly outputs a 3D voxel segmentation with per-instance labels. The authors claim it is the first framework to cover four anatomies—femur, pelvis, spine, and rib—totaling 54 bone categories, without per-anatomy preprocessing such as cropping individual vertebrae. Across nine datasets derived from five public CT collections, they report that Swin-X2S achieves the best Dice, Hausdorff distance, localization error, and identification rate among the methods compared, and also the best femoral head radius and neck-shaft angle among tested models. The clinical motivation is that X-ray is cheap and low-dose, so reliable 2D-to-3D reconstruction could provide 3D bone geometry for diagnosis and surgical planning where CT is unavailable or undesirable, particularly for children and low-resource settings. All experiments use DRR-synthesized images rather than real X-rays, so the paper's clinical promise rests on how well DRR training transfers to real patients.

What carries the argument

The load-bearing components are (1) a 2D Swin Transformer encoder with windowed and shifted-window self-attention that captures long-range context in each X-ray; (2) a dimension-expanding module that aligns the coronal and sagittal feature maps into a common 3D volume via 1x1x1 3D convolutions, bridging the 2D-pixel-to-3D-voxel gap; and (3) a 3D U-shaped convolution decoder whose bottleneck applies cross-attention between the two views, letting one view recover structure occluded in the other. The training loss combines per-view DiceCE supervision with a KL-divergence cross-view consistency term that encourages the two views to agree. This machinery is what lets a single model output voxel-level segmentation and per-instance labels for all 54 classes directly from uncropped X-rays.

What would settle it

Collect paired real X-ray and CT images for the same patients, run the released Swin-X2S model, and compare its Dice, HD, localization error, and identification rate against CT ground truth; a large drop relative to the reported DRR results would show that DRR training does not transfer to clinical X-rays, which is the premise on which the clinical promise rests.

Watch

Extended reading notes

Core claim

The central claim is that a single encoder-decoder architecture can convert a biplanar X-ray pair into a full 3D labeled segmentation without any structure-specific preprocessing. The 2D Swin Transformer encoder extracts features from each view; a dimension-expanding module bridges the 2D-to-3D dimension gap by stacking coronal and sagittal features into a common volume with 1x1x1 convolutions; and a 3D U-shaped decoder with cross-attention at the bottleneck fuses the two views. The paper reports Swin-X2S-Base reaches 89.11% Dice on CTPelvic1K, 83.84% on CTSpine1K, and 57.51% on TotalSegmentator-All, with the best labeling and clinical morphometry results among the compared methods. It further claims to outperform prior work without requiring known vertebra locations or input cropping, which earlier spine reconstruction methods needed.

Load-bearing premise

The load-bearing premise is that DRR images generated by ray-casting CT volumes are faithful enough stand-ins for real clinical X-rays that a model trained on them will reconstruct real patient anatomy with the same accuracy.

Editorial extensions

If this is right

  • A single trained model can segment and label 54 bone categories across four anatomies directly from two uncropped orthogonal X-rays, removing the per-vertebra cropping and localization steps required by earlier spine methods.
  • Varying the number of DRR views from 1 to 20 changes Dice by less than 1%, so the paper argues biplanar input is the preferred operating point given radiation dose and scan time.
  • Ablation results attribute large gains to transformer skip connections, the cross-view KL loss, ImageNet-22K pretraining, and data augmentation; removing convolution skip connections makes the model untrainable.
  • On ribs, the reported nlDice above 80% with larger centerline errors indicates the dominant error is axial segment localization rather than shape curvature, which is the clinically relevant direction for fracture assessment.
  • The reported results are on DRR-synthesized images, and the paper identifies paired real X-ray/CT datasets as the urgent next step for clinical validation.

Reading between the lines

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

  • A concrete extension would be to fine-tune the released model on a small set of real X-ray/CT pairs; if DRR pretraining transfers, such fine-tuning may close much of the domain gap without a large real dataset.
  • Because the cross-view KL loss needs no ground-truth label, it opens a route to semi-supervised training on unpaired real X-rays, which could mitigate the DRR-to-real mismatch.
  • The dimension-expanding module is a generic 2D-to-3D bridge and could be reused for other biplanar reconstruction problems, including metal implants, foreign objects, or non-osseous structures mentioned as future work.
  • A head-to-head spine comparison against BX2S-Net without any vertebra-location preprocessing would isolate whether the reported advantage comes from the architecture itself or from removing the cropping requirement.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 7 minor

Summary. The paper proposes Swin-X2S, an end-to-end encoder-decoder that takes biplanar X-ray images as input and produces a 3D voxel segmentation with per-instance labels. The encoder is a 2D Swin Transformer, a dimension-expanding module converts 2D features to 3D, and a 3D U-shaped decoder with cross-view attention fuses the two viewpoints. The method is evaluated on nine DRR-based subsets derived from five public CT datasets, covering femur, pelvis, spine, and rib with 54 categories, and is compared against seven reimplemented baseline architectures. The main reported outcomes are Dice, HD, L-error, ID-rate, plus clinical morphometry and landmark metrics, together with ablations on the number of views, skip connections, cross loss, pretraining, and data augmentation.

Significance. If the reported DRR results transfer to real clinical X-rays, this would be a useful contribution to a clinically relevant inverse problem. The paper offers a broad evaluation across nine public datasets and four anatomies, releases code, and explicitly targets segmentation and labeling in one framework. The main strengths are the breadth of the benchmark, the inclusion of clinical morphometry metrics, and the ablation study. However, the current evidence is entirely synthetic: all inputs are digitally reconstructed radiographs rendered from the same CT volumes that provide the ground-truth segmentations. Since the motivating application is clinical, the external-validity gap is the decisive weakness. The architectural claims are internally consistent and plausible, but the clinical and 'directly from raw X-ray' claims are not supported by the present evaluation.

major comments (5)
  1. [Section 4.1 and Section 6] All training and evaluation use DRR images generated from CT volumes, with fixed known projection geometry and with ground-truth masks derived from the same CT scans. Section 6 concedes that DRR generation is 'a compromise solution due to the lack of datasets consisting of paired real X-ray images and aligned CT scans.' Because the abstract and introduction claim reconstruction 'directly from biplanar raw X-ray images' and clinical promise, this is a load-bearing external-validity gap. Please add an evaluation on real X-ray/CT pairs, even a small dataset, or substantially revise the claims to state that all results are on DRR-simulated X-rays and that transfer to real X-rays remains untested.
  2. [Tables 2 and 3] The main quantitative results are reported as single numbers without error bars, confidence intervals, or significance tests, and it is not stated whether these come from one run or multiple seeds. Several conclusions are drawn from small differences, such as Swin-X2S-Large versus Swin-X2S-Base on CTSpine1K (83.41% versus 83.84% Dice), and the 'state-of-the-art' claim depends on these comparisons. Please report mean and standard deviation over at least three training runs and, where appropriate, pairwise significance tests.
  3. [Section 5.2 and Appendix A] The seven comparison methods are reimplemented by the authors from the settings of [5], with the regression head replaced by a classification head, but no evidence is given that these reimplementations reproduce the original methods' performance. This creates a risk of unfair comparison. Please provide official implementations or pretrained weights when available, or demonstrate that the reimplementations match the original reported numbers on a shared benchmark.
  4. [Section 4.1] The DRR generation protocol is under-specified: the paper does not report source-to-detector distance, detector resolution, ray-casting integration scheme, attenuation model, intensity normalization, or any parameter values. Since every reported metric depends on these synthetic images, the benchmark is not reproducible from the text. Please release the generation script or provide the full protocol.
  5. [Section 5.3 and Figure 6] The clinical-metric results are shown only as scatter plots with regression lines; the actual mean values per method for FHR, NSA, pelvic landmark distances, vertebral parameters, and rib nlDice/LSCDError are not given in the text. The statement that 'Swin-X2S achieves the best femoral head radius (FHR) and neck shaft angle (NSA)' is therefore not directly verifiable. Please add a table with these numerical values and per-sample variability.
minor comments (7)
  1. [Abstract and Section 1] The phrase 'raw X-ray images' is used in several places, but all inputs are DRR-synthesized images. This wording should be qualified to avoid overstating the evaluation setting.
  2. [Table 1 and Section 4.4] Table 1 lists the Tiny model's Image Size as [96,96,128], which is inconsistent with the stated resampling to 128x128x160 in Section 4.4.
  3. [Equation (5)] The notation Q,K,V in R^{H×D×N} is inaccurate for attention matrices; these should be token-count by dimension, not image spatial dimensions.
  4. [Table 3] TL-Embedding's L-error on the TotalSegmentator-All dataset is reported as '-' with no explanation, which is confusing in a quantitative comparison table.
  5. [Appendix Table 6] The table uses the name 'AttentionNet' while the text refers to 'AttentionUNet', and the citation for 'UNet' points to Kasten et al. rather than to the original U-Net paper.
  6. [Section 5.4.1] The text describes a 'slight improvement' with more DRR views, but N=2 yields a lower Dice (83.23%) than N=1 (83.84%). The non-monotonic pattern should be described accurately.
  7. [Throughout] There are numerous typos, including 'state of art', 'preformed', and 'ti access' in Section 5.4.2, which should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: Swin-X2S is an empirical architecture paper; its reported metrics come from held-out public CT-derived DRR benchmarks and do not reduce to fitted inputs or self-citations by construction.

full rationale

I walked the paper's claimed derivation chain. The paper makes no analytic derivation or uniqueness claim; its central claim is empirical (a 2D Swin Transformer encoder plus a 3D U-shaped decoder with dimension-expanding modules reconstructs 3D segmentations and labels from biplanar DRR images). The supervised loss (Eqs. 7-8) compares predictions p_i and q_i with ground-truth y_i and is a standard segmentation objective, while the cross-view KL term (Eq. 9) is unsupervised. Reported Dice, HD, L-error, and ID-rate are evaluated on held-out test splits following official data splits described in Section 4.2, so the metrics are not fitted values renamed as predictions. Comparison methods are reimplemented under the external benchmark of reference [5], not under a self-citation, and no load-bearing uniqueness theorem or ansatz is imported from the authors' own prior work. The one genuine concern is external validity, not circularity: all X-ray inputs are DRRs synthesized from the same CT volumes that provide the ground-truth masks (Section 4.1), and Section 6 concedes 'DRR generation serves as a compromise solution due to the lack of datasets consisting of paired real X-ray images and aligned CT scans.' That synthetic-to-real gap, including real scatter, beam hardening, unknown pose and FOV, is a validation risk that should be weighed in assessing clinical claims, but it does not make the reported benchmark numbers equivalent to the paper's inputs by construction. Since no equation, fitted parameter, or self-citation chain reduces the central result to its own assumptions, the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard supervised deep learning assumptions plus domain choices: synthetic DRRs stand in for real X-rays; public dataset labels are ground truth; reimplemented baselines are fair; and several hand-chosen hyperparameters (loss weight, number of views, embedding sizes, window size, resolution) are not derived from theory. No new physical entities are introduced.

free parameters (5)
  • Loss weighting coefficient = 1/2 for each single-view and cross-view term
    Equation (7) weights L_single and L_cross equally; no sensitivity analysis or tuning procedure is reported, so the balance is a hand-chosen constant that affects optimization.
  • Number of DRR views N = 1 (biplanar)
    Ablation Table 4 shows N=1 gives Dice 83.84%; N=4 and N=10 give 83.99% and 84.04%. The paper's biplanar framing depends on this choice, which trades radiation dose for small accuracy gains.
  • Swin window size = 7x7
    Taken from Swin Transformer defaults; the authors do not test other window sizes for this task.
  • Embedding dimensions for model variants = 32/64/96/128
    Hand-selected to define Tiny/Small/Base/Large; model size is a free design choice and performance differences across variants are reported without significance testing.
  • Voxel grid resolution = 128x128x160
    All CTs are resampled to this isotropic grid (Section 4.4); this fixed resolution caps the achievable shape detail and is a hand-chosen trade-off between compute and fidelity.
assumptions (4)
  • domain assumption Digitally reconstructed radiographs (ray casting through CT) are a faithful proxy for clinical X-ray images for training and evaluation.
    Section 4.1 uses DRR to synthesize inputs; clinical applicability in Sections 1 and 6 depends on this proxy. The authors themselves call it a compromise in Section 6.
  • domain assumption Ground-truth labels in the nine public datasets are anatomically correct and consistently labeled.
    Section 4.2 assumes labels from TotalSegmentator, CTPelvic1K, CTSpine1K, VerSe'19 and RibSeg v2, including rib re-labeling by connectivity, are reliable targets for training and evaluation.
  • domain assumption The seven comparison methods, reimplemented per settings in [5] with classification heads swapped in, are fair baselines.
    Section 5.2 and Appendix A: baselines are reimplemented by the authors rather than using official released results; no code or seeds are specified.
  • domain assumption The external clinical metric pipelines (Fischer et al., Cerveri et al., Di Angelo and Di Stefano, Jin et al.) produce reliable measurements on reconstructed masks.
    Section 4.3.2 uses these pipelines; their errors on imperfect reconstructions are acknowledged in Section 5.3 but assumed acceptable for the reported clinical comparisons.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Swin-X2S: Reconstructing 3D Shape from 2D Biplanar X-ray with Swin Transformers." pith.science (2026). https://pith.science/paper/PJZODB6N

@misc{pith2026250105961,
  author       = {Pith},
  title        = {Pith review of: Swin-X2S: Reconstructing 3D Shape from 2D Biplanar X-ray with Swin Transformers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PJZODB6N}},
  note         = {Machine review of arXiv:2501.05961}
}
read the original abstract

The conversion from 2D X-ray to 3D shape holds significant potential for improving diagnostic efficiency and safety. However, existing reconstruction methods often rely on hand-crafted features, manual intervention, and prior knowledge, resulting in unstable shape errors and additional processing costs. In this paper, we introduce Swin-X2S, an end-to-end deep learning method for directly reconstructing 3D segmentation and labeling from 2D biplanar orthogonal X-ray images. Swin-X2S employs an encoder-decoder architecture: the encoder leverages 2D Swin Transformer for X-ray information extraction, while the decoder employs 3D convolution with cross-attention to integrate structural features from orthogonal views. A dimension-expanding module is introduced to bridge the encoder and decoder, ensuring a smooth conversion from 2D pixels to 3D voxels. We evaluate proposed method through extensive qualitative and quantitative experiments across nine publicly available datasets covering four anatomies (femur, hip, spine, and rib), with a total of 54 categories. Significant improvements over previous methods have been observed not only in the segmentation and labeling metrics but also in the clinically relevant parameters that are of primary concern in practical applications, which demonstrates the promise of Swin-X2S to provide an effective option for anatomical shape reconstruction in clinical scenarios. Code implementation is available at: \url{https://github.com/liukuan5625/Swin-X2S}.

Figures

Figures reproduced from arXiv: 2501.05961 by the authors.

Figure 1
Figure 1. The overall architecture of the proposed Swin-X2S network for CT Segmentation and labeling from biplanar X-ray images [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. An example of generated coronal group and sagittal [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Quantitative reconstruction results of proposed Swin-X2S-Base network with biplanar inputs. Top-left panel: results of Totalseg [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Reconstruction failure samples of Swin-X2S network on [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Comparison results of different methods on the CTPelvic1K, CTSpine1K, Totalsegmentatior-All datasets with biplanar inputs. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The correlation between the average Dice coefficient (x-axis) and clinical metrics (y-axis) on the Totalsegmentator-All dataset. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Ablation study of the key components of Swin-X2S on the CTSpine1K dataset with biplanar DRR inputs. Visualization results [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Visualization results of feature maps in Swin-X2S-Base [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 46 canonical work pages

  1. [5]

    Benchmarking encoder-decoder architectures for biplanar x-ray to 3d bone shape reconstruction

    Mahesh Shakya and Bishesh Khanal. Benchmarking encoder-decoder architectures for biplanar x-ray to 3d bone shape reconstruction. Advances in Neural Infor- mation Processing Systems, 36, 2024

    work page 2024

  2. [1]

    Spinal analysis using a three-dimensional radiographic technique

    Richard H Brown, Albert H Burstein, Clyde L Nash, and Charles C Schock. Spinal analysis using a three-dimensional radiographic technique. Journal of biomechanics, 9(6):355–IN1, 1976

    work page 1976

  3. [2]

    Fundamentals of computerized tomography: image reconstruction from projections

    Gabor T Herman. Fundamentals of computerized tomography: image reconstruction from projections . Springer Science & Business Media, 2009. �𝟏𝟏 𝟐𝟐 𝐑𝐑𝐑𝐑𝐑𝐑 �𝟏𝟏 𝟒𝟒 𝐑𝐑𝐑𝐑𝐑𝐑 �𝟏𝟏 𝟖𝟖 𝐑𝐑𝐑𝐑𝐑𝐑 �𝟏𝟏 𝟏𝟏𝟏𝟏 𝐑𝐑𝐑𝐑𝐑𝐑 �𝟏𝟏 𝟑𝟑𝟐𝟐 𝐑𝐑𝐑𝐑𝐑𝐑𝐅𝐅𝐅𝐅𝐅𝐅𝐅𝐅 𝐑𝐑𝐑𝐑𝐑𝐑 2D Swin3D Encoder3D Decoder Figure 8. Visualization results of feature maps in Swin-X2S-Base skip connections. The first row, the ...

    work page 2009

  4. [3]

    3d reconstruction from multi-view medical x-ray images–review and evalua- tion of existing methods

    S Hosseinian and H Arefi. 3d reconstruction from multi-view medical x-ray images–review and evalua- tion of existing methods. The international archives of the photogrammetry, remote sensing and spatial in- formation sciences, 40:319–326, 2015

    work page 2015

  5. [4]

    X2ct-gan: reconstruct- ing ct from biplanar x-rays with generative adversar- ial networks

    Xingde Ying, Heng Guo, Kai Ma, Jian Wu, Zhengxin Weng, and Yefeng Zheng. X2ct-gan: reconstruct- ing ct from biplanar x-rays with generative adversar- ial networks. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 10619–10628, 2019

    work page 2019

  6. [6]

    Medical imaging and nuclear 11 medicine: a lancet oncology commission

    Hedvig Hricak, May Abdel-Wahab, Rifat Atun, Miriam Mikhail Lette, Diana Paez, James A Brink, Llu´ıs Donoso-Bach, Guy Frija, Monika Hierath, Ola Holmberg, et al. Medical imaging and nuclear 11 medicine: a lancet oncology commission. The Lancet Oncology, 22(4):e136–e172, 2021

    work page 2021

  7. [7]

    Defining the diagnostic divide: an analysis of registered radiological equip- ment resources in a low-income african country

    Patrick Sitati Ngoya, Wilbroad Edward Muhogora, and Richard Denys Pitcher. Defining the diagnostic divide: an analysis of registered radiological equip- ment resources in a low-income african country. The Pan African Medical Journal, 25, 2016

    work page 2016

  8. [8]

    Toward au- tomated 3d spine reconstruction from biplanar radio- graphs using cnn for statistical spine model fitting

    Benjamin Aubert, Carlos Vazquez, Thierry Cresson, Stefan Parent, and Jacques A de Guise. Toward au- tomated 3d spine reconstruction from biplanar radio- graphs using cnn for statistical spine model fitting. IEEE transactions on medical imaging, 38(12):2796– 2806, 2019

    work page 2019

Show all 54 references
  1. [9]

    The eos™ imaging system and its uses in daily orthopaedic prac- tice

    Tam ´as Ill ´es and Szabolcs Somoske ¨oy. The eos™ imaging system and its uses in daily orthopaedic prac- tice. International orthopaedics, 36:1325–1331, 2012

    2012

  2. [10]

    Imagenet classification with deep convolutional neural networks

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hin- ton. Imagenet classification with deep convolutional neural networks. Advances in neural information pro- cessing systems, 25, 2012

    2012

  3. [11]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016

    2016

  4. [12]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems , 30, 2017

    2017

  5. [13]

    2d-to-3d: A review for computational 3d image reconstruction from x-ray images

    P Maken and A Gupta. 2d-to-3d: A review for computational 3d image reconstruction from x-ray images. DOI: https://doi. org/10.1007/s11831-022- 09790-z, pages 85–114, 2023

    2023 doi

  6. [14]

    3d reconstruc- tion method from biplanar radiography using non- stereocorresponding points and elastic deformable meshes

    D Mitton, C Landry, S Veron, Wata Skalli, F Lavaste, and Jacques A De Guise. 3d reconstruc- tion method from biplanar radiography using non- stereocorresponding points and elastic deformable meshes. Medical and Biological Engineering and Computing, 38:133–139, 2000

    2000

  7. [15]

    Three-dimensional surface rendering reconstruc- tion of scoliotic vertebrae using a non stereo- corresponding points technique

    A Mitulescu, Wata Skalli, D Mitton, and J De Guise. Three-dimensional surface rendering reconstruc- tion of scoliotic vertebrae using a non stereo- corresponding points technique. European spine journal, 11:344–352, 2002

    2002

  8. [16]

    3d/2d reg- istration and segmentation of scoliotic vertebrae us- ing statistical models

    Said Benameur, Max Mignotte, Stefan Parent, Hubert Labelle, Wafa Skalli, and Jacques de Guise. 3d/2d reg- istration and segmentation of scoliotic vertebrae us- ing statistical models. Computerized Medical Imaging and Graphics, 27(5):321–337, 2003

    2003

  9. [17]

    Jonathan Boisvert and Daniel C. Moura. Interactive 3d reconstruction of the spine from radiographs using a statistical shape model and second-order cone pro- gramming. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology So- ciety, pages 5726...

    2011

  10. [18]

    3d reconstruction of the lumbar vertebrae from anteroposterior and lateral dual-energy x-ray ab- sorptiometry

    Tristan Whitmarsh, Ludovic Humbert, Luis M Del R´ıo Barquero, Silvana Di Gregorio, and Alejandro F Frangi. 3d reconstruction of the lumbar vertebrae from anteroposterior and lateral dual-energy x-ray ab- sorptiometry. Medical image analysis, 17(4):475–487, 2013

    2013

  11. [19]

    3d femur model reconstruction from biplane x-ray images: a novel method based on laplacian surface deformation

    Vikas Karade and Bhallamudi Ravi. 3d femur model reconstruction from biplane x-ray images: a novel method based on laplacian surface deformation. Inter- national journal of computer assisted radiology and surgery, 10:473–485, 2015

    2015

  12. [20]

    Automatic seg- mentation of wrist bones in ct using a statistical wrist shape + pose model

    Emran Mohammad Abu Anas, Abtin Rasoulian, Alexander Seitel, Kathryn Darras, David Wilson, Paul St John, David Pichora, Parvin Mousavi, Robert Rohling, and Purang Abolmaesumi. Automatic seg- mentation of wrist bones in ct using a statistical wrist shape + pose model. IEEE trans...

    2016

  13. [21]

    Revisit- ing contour-driven and knowledge-based deformable models: Application to 2d-3d proximal femur recon- struction from x-ray images

    Christophe Ch ˆenes and J ´erˆome Schmid. Revisit- ing contour-driven and knowledge-based deformable models: Application to 2d-3d proximal femur recon- struction from x-ray images. In Medical Image Com- puting and Computer Assisted Intervention–MICCAI 2021: 24th International ...

    2021

  14. [22]

    Oral-3d: Reconstructing the 3d structure of oral cavity from panoramic x-ray

    Weinan Song, Yuan Liang, Jiawei Yang, Kun Wang, and Lei He. Oral-3d: Reconstructing the 3d structure of oral cavity from panoramic x-ray. In Proceedings of the AAAI conference on artificial intelligence , vol- ume 35, pages 566–573, 2021

    2021

  15. [23]

    Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learn- ing

    Liyue Shen, Wei Zhao, and Lei Xing. Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learn- ing. Nature biomedical engineering, 3(11):880–888, 2019

    2019

  16. [24]

    X-ctrsnet: 3d 12 cervical vertebra ct reconstruction and segmentation directly from 2d x-ray images

    Rongjun Ge, Yuting He, Cong Xia, Chenchu Xu, Weiya Sun, Guanyu Yang, Junru Li, Zhihua Wang, Hailing Yu, Daoqiang Zhang, et al. X-ctrsnet: 3d 12 cervical vertebra ct reconstruction and segmentation directly from 2d x-ray images. Knowledge-Based Sys- tems, 236:107680, 2022

    2022

  17. [25]

    Perspective projection-based 3d ct reconstruction from biplanar x-rays

    Daeun Kyung, Kyungmin Jo, Jaegul Choo, Joonseok Lee, and Edward Choi. Perspective projection-based 3d ct reconstruction from biplanar x-rays. In ICASSP 2023-2023 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP) , pages 1–5, 2023

    2023

  18. [26]

    End- to-end convolutional neural network for 3d recon- struction of knee bones from bi-planar x-ray images

    Yoni Kasten, Daniel Doktofsky, and Ilya Kovler. End- to-end convolutional neural network for 3d recon- struction of knee bones from bi-planar x-ray images. In Machine Learning for Medical Image Reconstruc- tion: Third International Workshop, MLMIR 2020, Held in Conjunction wit...

    2020

  19. [27]

    2d–3d reconstruction of distal fore- arm bone from actual x-ray images of the wrist using convolutional neural networks

    Ryoya Shiode, Mototaka Kabashima, Yuta Hiasa, Ku- nihiro Oka, Tsuyoshi Murase, Yoshinobu Sato, and Yoshito Otake. 2d–3d reconstruction of distal fore- arm bone from actual x-ray images of the wrist using convolutional neural networks. Scientific Reports, 11 (1):15249, 2021

    2021

  20. [28]

    Three-dimensional image volumes from two-dimensional digitally reconstructed radiographs: A deep learning approach in lower limb ct scans.Med- ical Physics, 48(5):2448–2457, 2021

    Diogo F Almeida, Patricio Astudillo, and Dirk Van- dermeulen. Three-dimensional image volumes from two-dimensional digitally reconstructed radiographs: A deep learning approach in lower limb ct scans.Med- ical Physics, 48(5):2448–2457, 2021

    2021

  21. [29]

    In- ferring the 3d standing spine posture from 2d radio- graphs

    Amirhossein Bayat, Anjany Sekuboyina, Johannes C Paetzold, Christian Payer, Darko Stern, Martin Urschler, Jan S Kirschke, and Bjoern H Menze. In- ferring the 3d standing spine posture from 2d radio- graphs. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2...

    2020

  22. [30]

    Image-to-graph convolutional net- work for deformable shape reconstruction from a sin- gle projection image

    Megumi Nakao, Fei Tong, Mitsuhiro Nakamura, and Tetsuya Matsuda. Image-to-graph convolutional net- work for deformable shape reconstruction from a sin- gle projection image. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conferenc...

    2021

  23. [31]

    Bx2s-net: Learning to reconstruct 3d spinal structures from bi- planar x-ray images

    Zheye Chen, Lijun Guo, Rong Zhang, Zhongding Fang, Xiuchao He, and Jianhua Wang. Bx2s-net: Learning to reconstruct 3d spinal structures from bi- planar x-ray images. Computers in Biology and Medicine, 154:106615, 2023

    2023

  24. [32]

    Multi-view vertebra localization and identification from ct images

    Han Wu, Jiadong Zhang, Yu Fang, Zhentao Liu, Nizhuan Wang, Zhiming Cui, and Dinggang Shen. Multi-view vertebra localization and identification from ct images. In International Conference on Med- ical Image Computing and Computer-Assisted Inter- vention, pages 136–145. Springer, 2023

    2023

  25. [33]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision , pages 10012–10022, 2021

    2021

  26. [34]

    U-net: Convolutional networks for biomedical im- age segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical im- age segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, par...

    2015

  27. [35]

    Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

    Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R Roth, and Daguang Xu. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In International MICCAI Brainlesion Workshop, pages 272–284, 2021

    2021

  28. [36]

    Swinmm: masked multi-view with swin transformers for 3d medical image segmen- tation

    Yiqing Wang, Zihan Li, Jieru Mei, Zihao Wei, Li Liu, Chen Wang, Shengtian Sang, Alan L Yuille, Cihang Xie, and Yuyin Zhou. Swinmm: masked multi-view with swin transformers for 3d medical image segmen- tation. In International Conference on Medical Im- age Computing and Compute...

    2023

  29. [37]

    Self-supervised pre-training of swin transformers for 3d medical image analysis

    Yucheng Tang, Dong Yang, Wenqi Li, Holger R Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. Self-supervised pre-training of swin transformers for 3d medical image analysis. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recogn...

    2022

  30. [38]

    V-net: Fully convolutional neural networks for volumetric medical image segmentation

    Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) , pages 565–571. Ieee, 2016

    2016

  31. [39]

    A mathematical theory of communication

    Claude Elwood Shannon. A mathematical theory of communication. The Bell system technical journal, 27 (3):379–423, 1948

    1948

  32. [40]

    Deep mutual learning

    Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu. Deep mutual learning. In Proceedings of 13 the IEEE conference on computer vision and pattern recognition, pages 4320–4328, 2018

    2018

  33. [41]

    Ct imaging based digi- tally reconstructed radiographs and their application in brachytherapy

    Natasa Milickovic, Dimos Baltas, S Giannouli, M La- hanas, and N Zamboglou. Ct imaging based digi- tally reconstructed radiographs and their application in brachytherapy. Physics in Medicine & Biology , 45 (10):2787, 2000

    2000

  34. [42]

    Totalsegmentator: Robust segmen- tation of 104 anatomic structures in ct images

    Jakob Wasserthal, Hanns-Christian Breit, Manfred T Meyer, Maurice Pradella, Daniel Hinck, Alexander W Sauter, Tobias Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, et al. Totalsegmentator: Robust segmen- tation of 104 anatomic structures in ct images. Radi- ology: Artificial In...

    2023

  35. [43]

    Ctspine1k: A large-scale dataset for spinal vertebrae segmentation in computed tomogra- phy

    Yang Deng, Ce Wang, Yuan Hui, Qian Li, Jun Li, Shiwei Luo, Mengke Sun, Quan Quan, Shuxin Yang, You Hao, et al. Ctspine1k: A large-scale dataset for spinal vertebrae segmentation in computed tomogra- phy. arXiv preprint arXiv:2105.14711, 2021

    2021 arXiv

  36. [44]

    Verse: a vertebrae labelling and segmenta- tion benchmark for multi-detector ct images

    Anjany Sekuboyina, Malek E Husseini, Amirhossein Bayat, Maximilian L ¨offler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kuka ˇcka, Christian Payer, Darko ˇStern, et al. Verse: a vertebrae labelling and segmenta- tion benchmark for multi-detector ct images. Medical image analys...

    2021

  37. [45]

    Ribseg v2: A large-scale benchmark for rib labeling and anatomical centerline extraction

    Liang Jin, Shixuan Gu, Donglai Wei, Jason Ken Adhinarta, Kaiming Kuang, Yongjie Jessica Zhang, Hanspeter Pfister, Bingbing Ni, Jiancheng Yang, and Ming Li. Ribseg v2: A large-scale benchmark for rib labeling and anatomical centerline extraction. IEEE Transactions on Medical Im...

    2023

  38. [46]

    Are we using appropriate segmentation metrics? identifying correlates of hu- man expert perception for cnn training beyond rolling the dice coefficient

    Florian Kofler, Ivan Ezhov, Fabian Isensee, Fabian Balsiger, Christoph Berger, Maximilian Koerner, Beatrice Demiray, Julia Rackerseder, Johannes Paet- zold, Hongwei Li, et al. Are we using appropriate segmentation metrics? identifying correlates of hu- man expert perception fo...

    2021 arXiv

  39. [47]

    Automated method for computing the morpho- logical and clinical parameters of the proximal fe- mur using heuristic modeling techniques

    Pietro Cerveri, Mario Marchente, Ward Bartels, Kristoff Corten, Jean-Pierre Simon, and Alfonso Man- zotti. Automated method for computing the morpho- logical and clinical parameters of the proximal fe- mur using heuristic modeling techniques. Annals of Biomedical Engineering, ...

    2010

  40. [48]

    A robust method for auto- matic identification of landmarks on surface models of the pelvis

    Maximilian CM Fischer, Felix Krooß, Juliana Habor, and Klaus Radermacher. A robust method for auto- matic identification of landmarks on surface models of the pelvis. Scientific Reports, 9(1):13322, 2019

    2019

  41. [49]

    A new method for the automatic identification of the dimensional fea- tures of vertebrae

    Luca Di Angelo and Paolo Di Stefano. A new method for the automatic identification of the dimensional fea- tures of vertebrae. Computer methods and programs in biomedicine, 121(1):36–48, 2015

    2015

  42. [50]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018

    2018

  43. [51]

    Learning a predictable and generative vector representation for objects

    Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta. Learning a predictable and generative vector representation for objects. InComputer Vision– ECCV 2016: 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part VI 14, pages 4...

    2016

  44. [52]

    Using bi-planar x-ray images to reconstruct the spine structure by the convolution neural network

    Chih-Chia Chen and Yu-Hua Fang. Using bi-planar x-ray images to reconstruct the spine structure by the convolution neural network. In Future Trends in Biomedical and Health Informatics and Cybersecu- rity in Medical Devices: Proceedings of the Interna- tional Conference on Bio...

    2019

  45. [53]

    Unetr: Transformers for 3d medical image segmentation

    Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R Roth, and Daguang Xu. Unetr: Transformers for 3d medical image segmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 574–584, 2022

    2022

  46. [54]

    Attention u-net: Learning where to look for the pancreas

    Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 , 2018. 14

    2018 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.