OSCR

Generalized plaque digitization framework for multi-dimensional mesoscopic images.

Code ↔ Paper

6 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 6 matches
  1. [1] § Materials and methods › 3D plaque spatial localization ↔ 2D_detection/mrcnn/config.py, lines 7–157 · score 0.80 · gradient clipping, ground truth, maximum suppression, decay, NMS, head
  2. [2] § Materials and methods › 2D plaque spatial localization ↔ 2D_detection/mrcnn/model.py, lines 1792–1858 · score 0.78 · attention maps, feature map, fused, sigmoid, activated, Pyramid
  3. [3] § Materials and methods › 3D plaque spatial localization ↔ 2D_detection/samples/Plaques/Plaques.py, lines 63–120 · score 0.58 · gradient clipping, decay, NMS, configurations, anchor, confidence
  4. [4] § Results › Multi-scale feature fusion and enhancement improves complex plaque detection ↔ 2D_detection/mrcnn/model.py, lines 1792–1858 · score 0.58 · attention maps, feature map, fuses, FPN, Fusion, enhancement
  5. [5] § Materials and methods › Foreground signal segmentation ↔ segmentation/seg_block.py, lines 436–489 · score 0.56 · Connected component, holes, edge, filling, volume, signals
  6. [6] § Results › Plaque-oriented training strategies facilitate model convergence and generalization ↔ 3D_detection/util.py, lines 253–367 · score 0.53 · Focal Loss, confidence loss, smoothly, weight, class, batches

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Python · 2,888 lines · 117 KB · no license · 2 matches

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: 2D_detection/mrcnn/model.py.

Overview

Authors: Guixuan Gong1, Xin Liu1, Xueyan Jia2, Ben Long3, Siqi Chen1, Tao Jiang2, Yue Luo1, Zhao Feng2,3, Xiangning Li2,3, Qingming Luo3, Hui Gong1,2, Anan Li1,2,3
ORCID iDs: Xin Liu, Tao Jiang, Zhao Feng, Qingming Luo, Hui Gong, Anan Li
  1. MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
  2. HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215123, China
  3. State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, 570228, China
Institutions: Wuhan National Laboratory for Optoelectronics (China); Huazhong University of Science and Technology (China); Hainan University (China); Jiangsu Industry Technology Research Institute (China)
Journal: Biomedical optics express, volume 17, issue 8, pages 4198-4215
Dates: received 12 May 2026; accepted 6 July 2026; published online 20 July 2026
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1364/boe.605322 · PMID 42610137 · PMCID PMC13481076 · OpenAlex W7168184877
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Connectivity, Machine learning
Topic: Medical Image Segmentation Techniques (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: Brain Science and Brain-like Intelligence Technology—National Science and Technology Major Project (2021ZD02010002, 2021ZD02010001); National Natural Science Foundation of China https://ror.org/01h0zpd94 (91749209); 111 Project (D23022)
Citations: cited by 1 paper (Europe PMC); 38 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

Brainsmatics/GPDigit

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cea2b38bfa09f29584a05a821771d2f25aaa8fa5, 7 May 2026
Languages: Python (38), Jupyter (2)
Size: 99 files, 40 scripts
Software Heritage: not archived
Found in: the references
Holds: README, environment (2D_detection/requirements.txt, 3D_detection/requirements.txt, label_revise/requirements.txt), 2 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (33 files), pandas (18 files), OpenCV (15 files), Matplotlib (12 files), scikit-image (10 files), PyTorch (7 files), SimpleITK (7 files), tifffile (7 files), TensorFlow (6 files), Pillow (3 files), Keras (2 files), SciPy (2 files), h5py (1 file), imageio (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
41 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit cea2b38, when its fingerprint is the one OSCR verified. How this works.

At the source: github.com/Brainsmatics/GPDigit

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;
  • 40 scripts, each with its path and the digest of its content;
  • 6 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

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1364/boe.605322.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 funders, 31 references.

Cite

This paper

Gong, G., Liu, X., Jia, X., Long, B., Chen, S., Jiang, T., Luo, Y., Feng, Z., Li, X., Luo, Q., Gong, H., & Li, A. (2026). Generalized plaque digitization framework for multi-dimensional mesoscopic images. Biomedical optics express, 17(8), 4198-4215. https://doi.org/10.1364/boe.605322

BibTeX

@article{gong2026generalized,
author = {Gong, Guixuan and Liu, Xin and Jia, Xueyan and Long, Ben and Chen, Siqi and Jiang, Tao and Luo, Yue and Feng, Zhao and Li, Xiangning and Luo, Qingming and Gong, Hui and Li, Anan},
title = {{Generalized plaque digitization framework for multi-dimensional mesoscopic images}},
journal = {Biomedical optics express},
year = {2026},
month = jul,
volume = {17},
number = {8},
pages = {4198--4215},
publisher = {Optica Publishing Group},
issn = {2156-7085},
doi = {10.1364/boe.605322},
url = {https://doi.org/10.1364/boe.605322},
pmid = {42610137},
pmcid = {PMC13481076}
}

RIS

TY - JOUR
AU - Gong, Guixuan
AU - Liu, Xin
AU - Jia, Xueyan
AU - Long, Ben
AU - Chen, Siqi
AU - Jiang, Tao
AU - Luo, Yue
AU - Feng, Zhao
AU - Li, Xiangning
AU - Luo, Qingming
AU - Gong, Hui
AU - Li, Anan
TI - Generalized plaque digitization framework for multi-dimensional mesoscopic images
T2 - Biomedical optics express
J2 - Biomed Opt Express
PY - 2026
DA - 2026/07/20
VL - 17
IS - 8
SP - 4198
EP - 4215
SN - 2156-7085
PB - Optica Publishing Group
DO - 10.1364/boe.605322
UR - https://doi.org/10.1364/boe.605322
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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