Browse State-of-the-Art › Medical Image Registration
Medical Image Registration
105 papers with code · 4 benchmarks · 8 datasets archive 2025-07-28
Image registration, also known as image fusion or image matching, is the process of aligning two or more images based on image appearances. Medical Image Registration seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. Medical Image Registration is used in many clinical applications such as image guidance, motion tracking, segmentation, dose accumulation, image reconstruction and so on. Medical Image Registration is a broad topic which can be grouped from various perspectives. From input image point of view, registration methods can be divided into unimodal, multimodal, interpatient, intra-patient (e.g. same- or different-day) registration. From deformation model point of view, registration methods can be divided in to rigid, affine and deformable methods. From region of interest (ROI) perspective, registration methods can be grouped according to anatomical sites such as brain, lung registration and so on. From image pair dimension perspective, registration methods can be divided into 3D to 3D, 3D to 2D and 2D to 2D/3D.
Source: Deep Learning in Medical Image Registration: A Review
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| IXI (8 rows) | LL_Net | A light-weight rectangular decomposition large kernel convolution... | code | — | Compare |
| OASIS (8 rows) | TransMorph | TransMorph: Transformer for unsupervised medical image registration | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
| OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP (1 row) | VoxelMorph | VoxelMorph: A Learning Framework for Deformable Medical Image Registration | code | — | Compare |
| SR-Reg (1 row) | MambaMorph | MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable... | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
8 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
2 subtasks in the archive's task tree.
Most implemented papers archive 2025-07-28
30 shown of 105 papers with code (198 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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14 Sep 2018 9 repositories listedIn contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
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13 Feb 2019 6 repositories listed3D medical image registration is of great clinical importance.
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29 Jul 2019 5 repositories listed Syntology ran 0 of 15 samples · 15 unverified · 8 pointer-only (licence)We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration.
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13 Jun 2022 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
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21 Mar 2019 3 repositories listedIn contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model.
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26 Jun 2018 3 repositories listedWith the "Autograd Image Registration Laboratory" (AIRLab), we introduce an open laboratory for image registration tasks, where the analytic gradients of the objective function are computed automatically and the device…
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7 Feb 2018 3 repositories listedWe define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
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25 Mar 2024 2 repositories listedDeformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions.
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25 Jan 2024 2 repositories listedCapturing voxel-wise spatial correspondence across distinct modalities is crucial for medical image analysis.
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30 Sep 2023 2 repositories listedRigid pre-registration involving local-global matching or other large deformation scenarios is crucial.
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29 Aug 2023 2 repositories listedWeakly-supervised methods struggle due to the scarcity of labeled data, and unsupervised methods directly depend on image similarity metrics for accuracy.
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28 Apr 2023 2 repositories listedInverse consistency is a desirable property for image registration.
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4 Nov 2022 2 repositories listed Syntology ran 3 of 10 samples · 7 unverifiedFor AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.
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2 Dec 2021 2 repositories listedThe computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis.
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19 Nov 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverifiedRecently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications.
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7 May 2021 2 repositories listed Syntology ran 0 of 19 samples · 19 unverifiedDeformable templates are essential to large-scale medical image registration, segmentation, and population analysis.
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9 Mar 2021 2 repositories listedIn this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations.
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26 Sep 2019 2 repositories listed Syntology ran 0 of 9 samples · 9 unverifiedWe propose a Dual-Stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration.
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8 Mar 2019 2 repositories listedWe present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional…
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30 Jul 2018 2 repositories listedThe method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight…
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15 Jul 2025 1 repository listedFoundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration.
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30 May 2025 1 repository listedRecent advances in deep learning-based medical image registration have shown that training deep neural networks~(DNNs) does not necessarily require medical images.
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10 May 2025 1 repository listedDeformable registration is a fundamental task in medical image processing, aiming to achieve precise alignment by establishing nonlinear correspondences between images.
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31 Mar 2025 1 repository listedImage registration is fundamental in medical imaging, enabling precise alignment of anatomical structures for diagnosis, treatment planning, image-guided interventions, and longitudinal monitoring.
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29 Mar 2025 1 repository listedThe OncoReg Challenge addresses this issue by enabling researchers to develop and validate image registration methods through a two-phase framework that ensures patient privacy while fostering the development of more…
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25 Mar 2025 1 repository listedDeep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds.
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19 Feb 2025 1 repository listedWe evaluate Triad across three tasks, namely, organ/tumor segmentation, organ/cancer classification, and medical image registration, in two data modalities (within-domain and out-of-domain) settings using 25 downstream…
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17 Feb 2025 1 repository listedThe framework includes: (1) Explicit anatomical information injection, where SAM-generated segmentation masks are used as auxiliary inputs throughout training and testing to ensure the consistency of anatomical…
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24 Dec 2024 1 repository listedHowever, the high computation and memory loads of SA (growing quadratically with the spatial resolution) hinder transformers from processing subtle textural information in high-resolution image features, e.
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23 Nov 2024 1 repository listedFinally, a hierarchical metric was proposed to evaluate the similarity of image pairs in both the original pixel space and latent-feature space, enhancing topology preservation while improving registration accuracy.
Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections