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An effective deep learning algorithm for medical image registration.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § Experiment › Implementation ↔ voxelmorph/__init__.py, lines 1–31 · score 0.63 · PyTorch, deep learning framework, loss function, Python
  2. [2] § Methodology › Dual multiscale feature extractor ↔ voxelmorph/nn/models.py, lines 60–124 · score 0.59 · ReLU, activation function, layers, channel, dimensions, Transformers
  3. [3] § Methodology › Similarity loss ↔ voxelmorph/nn/losses.py, lines 14–27 · score 0.57 · Normalized Cross Correlation, NCC, loss
  4. [4] § Experiment › Implementation ↔ scripts/train.py, lines 167–250 · score 0.54 · loss weights, setup, GPU, CPU, PyTorch, batch

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 53 lines · 1.6 KB · Apache-2.0 · 1 match

  1. """
  2. Deep learning tools for deformable medical image registration. This package
  3. offers reference implementations of core registration networks, loss
  4. functions, and utilities, with PyTorch and TensorFlow backends.
  6. ## Subpackages (overview)
  8. - **nn**: Torch-based neural network components for Voxelmorph.
  9. - **py**: Python utilities for Voxelmorph.
  11. ???+ quote "Citation"
  13. === "APA"
  14. Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J., & Dalca,
  15. A. V. (2018). VoxelMorph: A Learning Framework for Deformable
  16. Medical Image Registration. *arXiv:1809.05231*.
  17. <https://arxiv.org/abs/1809.05231>
  19. === "BibTeX"
  20. ```bibtex
  21. @article{balakrishnan2018voxelmorph,
  22. title = {VoxelMorph: A Learning Framework for Deformable Medical
  23. Image Registration},
  24. author = {Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R and
  25. Guttag, John and Dalca, Adrian V},
  26. journal = {arXiv preprint arXiv:1809.05231},
  27. year = {2018},
  28. url = {https://arxiv.org/abs/1809.05231}
  29. }
  30. ```
  31. """
  33. # set version
  34. __version__ = '0.3.3'
  37. # Third-party imports
  38. from packaging import version
  39. import neurite
  41. # ensure valid neurite version is available
  42. minv = '0.3'
  43. curv = getattr(neurite, '__version__', None)
  44. if curv is None or version.parse(curv) < version.parse(minv):
  45. raise ImportError(f'voxelmorph requires neurite version {minv} or greater, '
  46. f'but found version {curv}')
  48. # Local imports
  49. from . import nn
  50. from . import py
  51. from .functional import *
  53. __all__ = ['nn', 'py']

__init__.py at commit c4155e1, under Apache-2.0 · at the source

Overview

Authors: Jinqiu Deng1, Ke Chen2, Mingke Li1, Zhichao Zuo3, Alejandro F Frangi4, Jianping Zhang1
ORCID iDs: Jinqiu Deng, Ke Chen, Zhichao Zuo, Alejandro F Frangi, Jianping Zhang
  1. School of Mathematics and Computational Science, Xiangtan University, Xiangtan, China
  2. Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom
  3. Radiology Department of Xiangtan Central Hospital, Xiangtan, China
  4. Schools of Health Sciences and Computer Science, University of Manchester, Manchester, United Kingdom
Institutions: Xiangtan University (China); University of Strathclyde (United Kingdom); University of Manchester (United Kingdom)
Journal: PLOS digital health, volume 5, issue 4, article e0001339
Dates: received 23 December 2025; accepted 17 March 2026; published online 16 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pdig.0001339 · PMID 41990030 · PMCID PMC13086356 · OpenAlex W7154593092
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning
Topic: Medical Image Segmentation Techniques (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: Natural Science Foundation of Hunan Province (2023GK2029); National Natural Science Foundation of China (12201320, 12071402); Science and Technology Innovation Program of Hunan Province (2024WZ9008); Royal Academy of Engineering Chair INSILEX (CiET1819\19); UKRI Frontier Research Guarantee INSILICO (EP\Y030494\1)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

Image registration is crucial for many medical imaging applications, including longitudinal monitoring and multimodal information fusion. A key challenge is to achieve accurate alignment while strictly preserving topology and invertibility. To address the limitations of traditional penalty-based regularization, which may still permit local folding, this study proposes DTC-Reg, a dynamically learned registration framework that more explicitly enforces diffeomorphic deformation. The framework integrates a homotopy-based control–increment formulation with explicit multiscale geometric constraints. Two parameter-sharing U-Nets first extract multiscale feature pyramids from the input images, after which a symmetric registration module with a sequential temporal cascade network progressively refines the forward and inverse multiscale deformation fields. To further enhance diffeomorphic consistency, this study introduces a Multiscale Folding-aware Deformation Correction (MFDC) module that explicitly detects and geometrically rectifies folding points in the predicted deformation fields. Beyond its integration within DTC-Reg, MFDC can also be readily incorporated into several state-of-the-art registration networks, significantly reducing folding and improving deformation regularity. Extensive experiments on three 3D brain MRI registration tasks demonstrate that the proposed method consistently achieves superior performance over existing approaches in both quantitative and qualitative evaluations.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

voxelmorph/voxelmorph

License: Apache-2.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: c4155e1baf04bc8f1774775b23a6027b58b37e00, 11 September 2026
Languages: JavaScript (36), Python (20)
Size: 97 files, 56 scripts
Software Heritage: archived
Found in: “Data Availability”
Holds: README, license file, environment (pyproject.toml, setup.py, docs/requirements.txt), tests, documentation
Not found: CITATION.cff, continuous integration
Tools: PyTorch (13 files), NumPy (7 files), NiBabel (3 files), scikit-image (2 files), SciPy (2 files), h5py (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
58 files
At the source: github.com/voxelmorph/voxelmorph

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 56 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

All datasets used in this study are publicly available third-party data. The authors had no special access privileges to these datasets which others would not have. The specific data sources are as follows: 1. OASIS-v1 Dataset: The original data is available at https://www.oasis-brains.org/. For this study, we utilized the standard pre-processed version provided by Balakrishnan et al. (VoxelMorph), which can be accessed via their official repository at https://github.com/voxelmorph/voxelmorph or directly downloaded from https://github.com/adalca/medical-datasets/blob/master/neurite-oasis.md. 2. Patient-to-Atlas (IXI) Dataset: The original IXI dataset is available at http://brain-development.org/ixi-dataset/. We utilized the pre-processed version specifically formatted for registration tasks by Chen et al. (TransMorph), which is available via their GitHub repository at https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration. 3. Mindboggle101 Dataset: The raw data for the few-shot experiments (including NKI-RS-22, NKI-TRT-20, and OASIS-TRT-20 subsets) is publicly available at https://mindboggle.info/data.html.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 funders, 50 references.

Cite

This paper

Deng, J., Chen, K., Li, M., Zuo, Z., Frangi, A. F., & Zhang, J. (2026). An effective deep learning algorithm for medical image registration. PLOS digital health, 5(4), e0001339. https://doi.org/10.1371/journal.pdig.0001339

BibTeX

@article{deng2026effective,
author = {Deng, Jinqiu and Chen, Ke and Li, Mingke and Zuo, Zhichao and Frangi, Alejandro F and Zhang, Jianping},
title = {{An effective deep learning algorithm for medical image registration}},
journal = {PLOS digital health},
year = {2026},
month = apr,
volume = {5},
number = {4},
pages = {e0001339},
publisher = {PLOS},
issn = {2767-3170},
doi = {10.1371/journal.pdig.0001339},
url = {https://doi.org/10.1371/journal.pdig.0001339},
pmid = {41990030},
pmcid = {PMC13086356}
}

RIS

TY - JOUR
AU - Deng, Jinqiu
AU - Chen, Ke
AU - Li, Mingke
AU - Zuo, Zhichao
AU - Frangi, Alejandro F
AU - Zhang, Jianping
TI - An effective deep learning algorithm for medical image registration
T2 - PLOS digital health
J2 - PLOS Digit Health
PY - 2026
DA - 2026/04/16
VL - 5
IS - 4
SP - e0001339
SN - 2767-3170
PB - PLOS
DO - 10.1371/journal.pdig.0001339
UR - https://doi.org/10.1371/journal.pdig.0001339
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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