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REVIEW 4 major objections 5 minor 32 references

Gaussian Primitive Optimized Deformable Retinal Image Registration

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Gaussian-blended control nodes anchored at retinal vessels cut FIRE target registration error from 6.2 px to 2.35 px, the paper reports.

desk verdict A sensible sparse-node registration method whose headline numbers may be comparing apples to oranges unless the baselines were re-evaluated at the same resolution. read the letter →

arxiv 2508.16852 v1 pith:S63XPUWR submitted 2025-08-23 cs.CV cs.AIeess.IV

Xin Tian , Jiazheng Wang , Yuxi Zhang , Xiang Chen , Renjiu Hu , Gaolei Li , Min Liu , Hang Zhang This is my paper · ORCID
classification cs.CVcs.AIeess.IV
keywords retinalimageregistrationdeformableGaussianprimitivessparsefeaturepropagationkeypointcontrolnodesFIREdatasetiterativeoptimization
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

The paper sets out to solve a specific failure mode in deformable retinal image registration: most pixels are flat, textureless retina, while the informative vascular features occupy a small fraction of the image, so standard learning-based losses drown the alignment signal from vessels in noise. The proposed GPO framework places a sparse set of control nodes at keypoints on major vessels, models each node as a Gaussian primitive with learnable position, displacement, and radius, and propagates the nodes' displacements through a K-nearest-neighbour Gaussian blend to build a dense, globally coherent displacement field. It then iterates the node parameters with a loss that combines keypoint cross-correlation and global intensity alignment. On the FIRE dataset the paper reports a target registration error of 2.35 px and an AUC@25 px of 0.94, down from 6.20 px and 0.77 for the best descriptor-only baseline, and better than the strongest learning-based baseline. The significance is that the method shows how to keep gradient information flowing from sparse anatomical features into homogeneous regions, a problem that limits both classical and deep registration systems.

What carries the argument

The load-bearing mechanism is KNN-based Gaussian blending: a displacement field u(x) = sum over the K nearest control nodes of a normalized Gaussian weight times that node's displacement vector, where each weight depends on distance from x to the node's position and on the node's learned radius. Each node is a Gaussian primitive with trainable position, displacement, and radius, so the spatial influence of each anchor can adapt to local deformation scale. This construction performs structured message passing: high-gradient pixels near vascular keypoints feed gradients back to their nearby nodes through the same weights, and the K-nearest restriction keeps the field locally detailed while lim

What would settle it

Register the four FIRE Category A pairs (anatomical changes) with GPO-DCN and compare per-pair TRE to the reported 2.35 px average; if the sparse weighted-average representation cannot capture true structural change, those pairs should show TRE far above the average, directly testing the representation's coverage.

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Extended reading notes

Core claim

The paper's central claim is that deformable retinal registration can be reduced to optimizing a sparse set of Gaussian primitives rather than predicting a dense flow. Each primitive is a control point located on a salient vessel or grid site, with a learnable position, displacement vector, and radius. A K-nearest-neighbour Gaussian interpolation turns the primitives' displacements into a globally coherent displacement field, and gradient-based iteration over the node parameters, guided by a loss that combines keypoint cross-correlation with intensity alignment, refines the warp. On the FIRE benchmark this reaches 2.352 px mean target registration error and 0.938 AUC at 25 px, surpassing bot

Load-bearing premise

The method assumes every retinal deformation can be captured as a locally smooth weighted average of the translations carried by a few hundred control points, so deformations involving true anatomical change or very large undetected warps may not fit that representation.

Editorial extensions

If this is right

  • On the FIRE dataset, GPO-DCN achieves a target registration error of 2.352 px versus 6.201 px for GeoFormer and 2.766 px for RetinaRegNet, and higher AUC at 15, 25, and 50 px thresholds, indicating both better mean accuracy and fewer large outlier errors.
  • Anatomically placed descriptor-based nodes outperform uniform grid nodes (GPO-DCN 2.35 px vs GPO-GCN 2.65 px TRE), confirming that anchoring primitives at salient vascular structures matters.
  • The KNN Gaussian interpolation with K=10, N=1000 nodes, and 100 iterations provides a practical accuracy/runtime trade-off, roughly 30 seconds per FIRE pair at 1024x1024 resolution.
  • Because the displacement field is parameterized by sparse nodes rather than a dense per-pixel flow, the method does not need dense correspondence prediction and avoids the vanishing-gradient problem in flat retinal regions.
  • The framework also works with grid-based control nodes when descriptors are unavailable, at a modest accuracy cost, so it does not depend on a specific keypoint detector.

Reading between the lines

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

  • Not explored in the paper: the same Gaussian-primitive parametrization could be applied to other sparse-feature medical images, such as OCT, X-ray, or microscopy, where textureless backgrounds dominate and dense descriptors are unreliable.
  • Because FIRE Category A (anatomical change) contains only 4 pairs, the paper's pooled numbers may not reflect those cases; a per-category breakdown would test whether the sparse Gaussian blend can represent genuine structural change.
  • A direct ablation of the two loss terms (keypoint consistency only vs intensity only) would clarify which term actually carries the vessel-alignment signal, a claim the current experiments leave implicit.
  • The learnable node positions suggest an adaptive variant that grows or prunes primitives based on local gradient magnitude, potentially improving accuracy without fixing the node count N.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This manuscript presents Gaussian Primitive Optimization (GPO), an iterative deformable registration framework for retinal images. The pipeline first applies a coarse alignment (using GeoFormer) and extracts matched keypoints that serve as descriptor-based control nodes (DCN). Each node is modeled as a Gaussian primitive with learnable position, displacement, and radius; a K-nearest-neighbor Gaussian blending step converts the sparse node displacements into a dense displacement field. The node parameters are refined iteratively under a multi-term loss combining control-node cross-correlation and image normalized cross-correlation. On the FIRE dataset, the authors report that GPO-DCN reduces target registration error from 6.201 px (GeoFormer) to 2.352 px and increases AUC@25px from 0.770 to 0.938, with additional ablations over the number of nodes N, the number of neighbors K, and the number of iterations tau.

Significance. The core idea is well motivated by the gradient-dilution problem in retinal registration, and the use of sparse, anatomically anchored Gaussian primitives refined with iterative optimization is both intuitive and potentially useful. If the reported quantitative gains are obtained under a common evaluation protocol, the improvement over strong baselines is substantial. The paper also includes an ablation study and provides a public code repository. However, the current experimental description leaves the comparability of Table 1 unverified, and the lack of statistical confidence measures on a small test set makes the significance conditional on the requested clarifications.

major comments (4)
  1. [§3.1, Table 1] The paper states 'For all experiments, we resized images to 1024×1024' but does not state whether the GeoFormer, RetinaRegNet, SuperPoint, RoMa, and other baseline numbers in Table 1 were recomputed at this resolution or taken from published native-resolution (2912×2912) FIRE papers. The listed baseline values are in the range of published native-resolution results. If GPO is evaluated at 1024×1024 while the baselines are at 2912×2912, the comparison is invalid: a GPO TRE of 2.352 px at 1024×1024 would scale to roughly 6.69 px at native resolution, which does not beat GeoFormer's 6.201 px; similarly, AUC thresholds are not resolution-invariant. Please state the exact evaluation resolution for every row, how landmarks were handled across resolutions, and provide the evaluation script or raw per-pair errors. This is required to make the central claim verifiable.
  2. [§3.2, Table 1] With a 7:1:2 split on the 134 FIRE pairs, the test set is only about 27 pairs. Table 1 reports point estimates only, with no standard deviations, confidence intervals, or significance tests. The reported improvement of GPO-DCN (2.352 px) over RetinaRegNet (2.766 px) is about 0.4 px and could be within noise on this test size. Please report per-pair TRE distributions, error bars, paired significance tests, and per-category (S/A/P) results, especially for the four anatomical-change pairs, which stress the smoothness assumption of Eq. (3).
  3. [§3.2, Fig. 4 and Implementation Details] The final hyperparameters N=1000, K=10, tau=100, alpha_gcc=0.4, and alpha_ncc=1.0 appear to be selected from the ablations in Fig. 4, but the paper does not state whether those ablations were performed on the validation split or on the test split. Without a clearly held-out validation procedure, the reported test numbers may be a result of selection on the test set. Please specify which split was used for the ablations, and confirm that the final Table 1 numbers were obtained with hyperparameters fixed on validation only.
  4. [§2.2, Eq. (3)] The displacement field is computed by normalizing Gaussian weights only over the K nearest nodes at each pixel. Because the set of K nearest neighbors changes with x, u(x) can be discontinuous at locations where a neighbor enters or leaves the K-nearest set, even if the Gaussian weights themselves are continuous. This appears to contradict the claim of a 'smoothly varying displacement field' and may create seams or artifacts in the deformation. Normalizing over all N nodes, or otherwise constraining the blending to be partition-of-unity over a fixed set, would avoid this issue; at minimum, the authors should analyze or quantify the effect of this non-smoothness on the final registration.
minor comments (5)
  1. [§2.1] The paper says the descriptor network provides 'N matched keypoints', but does not explain how exactly N=1000 matches are selected when the network produces more or fewer matches. Please describe the matching and selection procedure.
  2. [§2.2, Eq. (3)] The displayed sum contains a typographical 'KX' notation; it should be a standard sum over i=1..K. Also, the radius parameterization includes rmin and rmax but their numerical values are not given in the Implementation Details.
  3. [§3.1] For baselines, it is unclear which methods were retrained on FIRE, which were used off-the-shelf, and which were evaluated from published numbers. This information is necessary for reproducibility and for judging the fairness of the comparison.
  4. [Figure 1] The gradient heatmaps lack a color scale and a detailed description of the preprocessing and normalization used. This makes the claimed difference in gradient propagation difficult to assess.
  5. [§3.2] The ablation text reports 'median TRE' in several places, while Table 1 and the main text report 'TRE' as an average. Please clarify which summary statistic is used in each figure and table.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: GPO is a self-contained empirical method; reported gains are measured outputs, not fitted predictions.

full rationale

The paper's central claim is an empirical result: after defining a parametric displacement field as a KNN-weighted Gaussian blend of sparse node translations (Eq. 3), it optimizes node parameters against keypoint and intensity losses, then measures TRE and AUC on held-out expert landmarks. No parameter is fitted to the evaluation landmarks, and the improvement over GeoFormer is not a tautology: GPO-DCN starts from GeoFormer's coarse alignment and adds an independent iterative refinement. The modeling of the deformation field is an explicit design choice, not a derivation whose conclusion is embedded in its premises. Self-citations to prior message-passing works are motivational only and are not load-bearing for the numerical results. The most important validity concern is the unstated coordinate frame of baseline numbers (1024 vs 2912), but that is an evaluation-fairness/correctness issue, not circularity in the sense of an output equaling an input by construction.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim depends on a smooth Gaussian-blending displacement representation, reliable initial correspondences, and intensity-based fidelity. No new physical entities are introduced; Gaussian primitives are computational constructs. Several hyperparameters (K, N, tau, loss weights, radius bounds) are hand-picked or tuned on FIRE without a reported held-out validation process, which is the main ledger cost.

free parameters (6)
  • K (number of nearest neighbors) = 10
    Chosen by ablation on FIRE to balance TRE and runtime; not derived from theory.
  • N (number of control nodes) = 1000 for DCN
    Chosen by ablation; more nodes improve TRE slightly but increase runtime.
  • tau_max (number of iterations) = 100 (DCN), 200 (GCN)
    Chosen by ablation for the accuracy/runtime tradeoff.
  • Loss weights alpha_gcc and alpha_ncc = 0.4 and 1.0
    Set by hand; no sensitivity analysis is reported.
  • Learning rates eta_g, eta_t, eta_r = 1.0, 0.01, 0.01
    Set by hand to allow fast position adjustment while preserving local fidelity.
  • Radius bounds rmin and rmax = not reported
    The radius reparametrization ri = rmin + (rmax - rmin) sigma(beta_i) + 0.1 depends on rmin and rmax, but their values are omitted.
assumptions (4)
  • domain assumption The true displacement field can be represented as a smooth KNN-Gaussian weighted combination of sparse node translations (Eq. 3).
    This is the core representation assumption in Sec. 2.2; deformations not captured by the control-node geometry cannot be recovered.
  • domain assumption Descriptor-based keypoints from the coarse network (e.g., GeoFormer) provide reliable initial correspondences for DCN.
    Sec. 2.1 initializes t_i from matched keypoints; incorrect matches bias the displacement field from the start.
  • domain assumption Intensity-based NCC and global cross-correlation are valid fidelity measures for retinal image pairs.
    Sec. 2.3 minimizes L_gcc and L_ncc; if illumination or modality differences break intensity correspondence, the optimization signal is unreliable.
  • domain assumption The FIRE expert landmarks and the 7:1:2 split provide a representative evaluation of registration quality.
    Sec. 3.1 evaluates on 10 landmarks per pair and a small split; only 4 pairs are in Category A, limiting conclusions about anatomical-change cases.

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Cite this review

Pith. "Pith review of Gaussian Primitive Optimized Deformable Retinal Image Registration." pith.science (2026). https://pith.science/paper/S63XPUWR

@misc{pith2026250816852,
  author       = {Pith},
  title        = {Pith review of: Gaussian Primitive Optimized Deformable Retinal Image Registration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S63XPUWR}},
  note         = {Machine review of arXiv:2508.16852}
}
read the original abstract

Deformable retinal image registration is notoriously difficult due to large homogeneous regions and sparse but critical vascular features, which cause limited gradient signals in standard learning-based frameworks. In this paper, we introduce Gaussian Primitive Optimization (GPO), a novel iterative framework that performs structured message passing to overcome these challenges. After an initial coarse alignment, we extract keypoints at salient anatomical structures (e.g., major vessels) to serve as a minimal set of descriptor-based control nodes (DCN). Each node is modelled as a Gaussian primitive with trainable position, displacement, and radius, thus adapting its spatial influence to local deformation scales. A K-Nearest Neighbors (KNN) Gaussian interpolation then blends and propagates displacement signals from these information-rich nodes to construct a globally coherent displacement field; focusing interpolation on the top (K) neighbors reduces computational overhead while preserving local detail. By strategically anchoring nodes in high-gradient regions, GPO ensures robust gradient flow, mitigating vanishing gradient signal in textureless areas. The framework is optimized end-to-end via a multi-term loss that enforces both keypoint consistency and intensity alignment. Experiments on the FIRE dataset show that GPO reduces the target registration error from 6.2\,px to ~2.4\,px and increases the AUC at 25\,px from 0.770 to 0.938, substantially outperforming existing methods. The source code can be accessed via https://github.com/xintian-99/GPOreg.

Figures

Figures reproduced from arXiv: 2508.16852 by the authors.

Figure 1
Figure 1. Visualization of gradient backflow in a retinal image under normalized cross￾correlation (NCC). The original image (left) is preprocessed and normalized to [0,1]. Heatmaps (right) show the absolute NCC gradients for x-axis shifts of 16, 32, and 48 pixels. High responses indicate effective gradient propagation; low responses correspond to homogeneous or vessel-sparse regions. difficult due to the dominance of large h… view at source ↗
Figure 2
Figure 2. Overview of GPO: control node initialization, KNN-based Gaussian blending, and iterative parameter updates. network provides N matched keypoints {(g f i , gm i )} N i=1 in If and I (coarse) m , forming descriptor-based control nodes (DCN) ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison on the FIRE dataset [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Ablation study on key parameters on TRE and running time. Left: Influence of the number of control nodes N. Middle: Effect of the number of iterations. Right: Impact of the number of nearest neighbors K. Ablation Studies We conducted a three-way ablation to examine how…

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Reference graph

Works this paper leans on

32 extracted references · 31 canonical work pages

  1. [1]

    Insight j 2(365), 1–35 (2009)

    Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ANTS). Insight j 2(365), 1–35 (2009)

    work page 2009

  2. [2]

    IEEE Transactions on Neural Networks and Learning Systems (2024)

    Chen, X., Liu, M., Wang, R., Hu, R., Liu, D., Li, G., Wang, Y., Zhang, H.: Spa- tially covariant image registration with text prompts. IEEE Transactions on Neural Networks and Learning Systems (2024)

    work page 2024

  3. [3]

    Medical image analysis52, 128–143 (2019)

    De Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., Išgum, I.: A deep learning framework for unsupervised affine and deformable image regis- tration. Medical image analysis52, 128–143 (2019)

    work page 2019

  4. [4]

    In: Proceedings of the IEEE conference on com- puter vision and pattern recognition workshops

    DeTone, D., Malisiewicz, T., Rabinovich, A.: Superpoint: Self-supervised interest point detection and description. In: Proceedings of the IEEE conference on com- puter vision and pattern recognition workshops. pp. 224–236 (2018)

    2018

  5. [5]

    In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition

    Edstedt, J., Sun, Q., Bökman, G., Wadenbäck, M., Felsberg, M.: Roma: Robust dense feature matching. In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition. pp. 19790–19800 (2024)

    work page 2024

  6. [6]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Heinrich, M.P.: Closing the gap between deep and conventional image registra- tion using probabilistic dense displacement networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 50–58. Springer (2019)

    work page 2019

  7. [7]

    In: International workshop on biomedical image registration

    Heinrich, M.P., Hansen, L.: Voxelmorph++ going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation. In: International workshop on biomedical image registration. pp. 85–95. Springer (2022)

    work page 2022

  8. [8]

    IEEE transactions on medical imaging 32(7), 1239–1248 (2013)

    Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE transactions on medical imaging 32(7), 1239–1248 (2013)

    work page 2013

Show all 32 references
  1. [9]

    Modeling and Artificial Intel- ligence in Ophthalmology1(4), 16–28 (2017)

    Hernandez-Matas, C., Zabulis, X., Triantafyllou, A., Anyfanti, P., Douma, S., Ar- gyros, A.A.: Fire: fundus image registration dataset. Modeling and Artificial Intel- ligence in Ophthalmology1(4), 16–28 (2017)

    2017

  2. [10]

    Frontiers in cell and developmental biology9, 659941 (2021) 10 X

    Hu, J., Wang, H., Cao, Z., Wu, G., Jonas, J.B., Wang, Y.X., Zhang, J.: Automatic artery/vein classification using a vessel-constraint network for multicenter fundus images. Frontiers in cell and developmental biology9, 659941 (2021) 10 X. Tian et al

    2021

  3. [11]

    IEEE Transactions on Medical Imaging 41(1), 199–212 (2021)

    Jia, X., Thorley, A., Chen, W., Qiu, H., Shen, L., Styles, I.B., Chang, H.J., Leonardis, A., De Marvao, A., O’Regan, D.P., et al.: Learning a model-driven vari- ational network for deformable image registration. IEEE Transactions on Medical Imaging 41(1), 199–212 (2021)

    2021

  4. [12]

    IEEE transactions on medical imaging 29(1), 196–205 (2009)

    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a tool- box for intensity-based medical image registration. IEEE transactions on medical imaging 29(1), 196–205 (2009)

    2009

  5. [13]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Lindenberger, P., Sarlin, P.E., Pollefeys, M.: LightGlue: Local feature matching at light speed. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 17627–17638 (2023)

    2023

  6. [14]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Liu,J.,Li,X.:Geometrizedtransformerforself-supervisedhomographyestimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9556–9565 (2023)

    2023

  7. [15]

    In: European Conference on Computer Vision

    Liu, J., Li, X., Wei, Q., Xu, J., Ding, D.: Semi-supervised keypoint detector and de- scriptor for retinal image matching. In: European Conference on Computer Vision. pp. 593–609. Springer (2022)

    2022

  8. [16]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Mok, T.C., Chung, A.: Affine medical image registration with coarse-to-fine vision transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20835–20844 (2022)

    2022

  9. [17]

    Mok, T.C., Chung, A.C.: Large deformation image registration with anatomy- aware laplacian pyramid networks. In: Segmentation, Classification, and Registra- tion of Multi-modality Medical Imaging Data: MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunct...

    2020

  10. [18]

    Biomedical Physics & Engineering Express3(4), 045015 (2017)

    Noyel, G., Thomas, R., Bhakta, G., Crowder, A., Owens, D., Boyle, P.: Superim- position of eye fundus images for longitudinal analysis from large public health databases. Biomedical Physics & Engineering Express3(4), 045015 (2017)

    2017

  11. [19]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Potje, G., Cadar, F., Araujo, A., Martins, R., Nascimento, E.R.: XFeat: Acceler- ated features for lightweight image matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2682–2691 (2024)

    2024

  12. [20]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Qiu, H., Hammernik, K., Qin, C., Chen, C., Rueckert, D.: Embedding gradient- based optimization in image registration networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 56–65. Springer (2022)

    2022

  13. [21]

    Advances in neural information processing sys- tems 32 (2019)

    Revaud, J., De Souza, C., Humenberger, M., Weinzaepfel, P.: R2D2: Reliable and repeatable detector and descriptor. Advances in neural information processing sys- tems 32 (2019)

    2019

  14. [22]

    Computers in Biology and Medicine186, 109645 (2025)

    Sivaraman,V.B.,Imran,M.,Wei,Q.,Muralidharan,P.,Tamplin,M.R.,Grumbach, I.M., Kardon, R.H., Wang, J.K., Zhou, Y., Shao, W.: Retinaregnet: A zero-shot approach for retinal image registration. Computers in Biology and Medicine186, 109645 (2025)

    2025

  15. [23]

    In: 2022 IEEE International Conference on Image Processing (ICIP)

    Tian, X., Anantrasirichai, N., Nicholson, L., Achim, A.: Optimal transport-based graph matching for 3d retinal oct image registration. In: 2022 IEEE International Conference on Image Processing (ICIP). pp. 2791–2795. IEEE (2022)

    2022

  16. [24]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Tian, X., Anantrasirichai, N., Nicholson, L., Achim, A.: Tagat: Topology-aware graph attention network for multi-modal retinal image fusion. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 775–784. Springer (2024)

    2024

  17. [25]

    In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

    Tian, X., Zheng, R., Chu, C.J., Bell, O.H., Nicholson, L.B., Achim, A.: Multi- modal retinal image registration and fusion based on sparse regularization via a Gaussian Primitive Optimized Deformable Retinal Image Registration 11 generalized minimax-concave penalty. In: ICASSP...

    2019

  18. [26]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Truong, P., Apostolopoulos, S., Mosinska, A., Stucky, S., Ciller, C., Zanet, S.D.: Glampoints: Greedily learned accurate match points. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10732–10741 (2019)

    2019

  19. [27]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Zhang, H., Chen, X., Hu, R., Liu, D., Li, G., Wang, R.: MemWarp: Discontinuity- preserving cardiac registration with memorized anatomical filters. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 671–681. Springer (2024)

    2024

  20. [28]

    arXiv preprint arXiv:2506.10813 (2025)

    Zhang, H., Chen, X., Hu, R., Wang, R., Zhang, J., Liu, M., Wang, Y., Li, G., Cheng, X., Duan, J.: Unsupervised deformable image registration with structural nonparametric smoothing. arXiv preprint arXiv:2506.10813 (2025)

    2025 arXiv

  21. [29]

    arXiv preprint arXiv:2401.09833 (2024)

    Zhang, H., Chen, X., Wang, R., Hu, R., Liu, D., Li, G.: Slicer networks. arXiv preprint arXiv:2401.09833 (2024)

    2024 arXiv

  22. [30]

    arXiv preprint arXiv:2301.07895 (2023)

    Zhang, H., Wang, R., Zhang, J., Liu, D., Li, C., Li, J.: Spatially covariant lesion segmentation. arXiv preprint arXiv:2301.07895 (2023)

    2023 arXiv

  23. [31]

    IEEE journal of biomedical and health informatics24(5), 1394–1404 (2019)

    Zhao, S., Lau, T., Luo, J., Eric, I., Chang, C., Xu, Y.: Unsupervised 3D end-to- end medical image registration with volume tweening network. IEEE journal of biomedical and health informatics24(5), 1394–1404 (2019)

    2019

  24. [32]

    In: Annual Confer- ence on Medical Image Understanding and Analysis

    Zheng, J.Q., Wang, Z., Huang, B., Vincent, T., Lim, N.H., Papież, B.W.: Recursive deformable image registration network with mutual attention. In: Annual Confer- ence on Medical Image Understanding and Analysis. pp. 75–86. Springer (2022)

    2022

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