High-fidelity 3D geometric reconstruction of pelvic organs from MRI: a hybrid deep learning and iterative optimization approach
- DOI
- 10.1088/1361-6560/aeb598
- Published
- 2026-11-09
- Container
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1088/1361-6560/aeb598,
title = {High-fidelity 3D geometric reconstruction of pelvic organs from MRI: a hybrid deep learning and iterative optimization approach},
author = {Hui Wang and Xiaowei Li and Chenxin Zhang and Yifan Feng and Jianwei Zuo and Yumeng Tang and Xiuli Sun and Jianliu Wang and Bing Xie and Jiajia Luo},
year = {2026},
journal = {Physics in Medicine \& Biology},
doi = {10.1088/1361-6560/aeb598},
url = {https://doi.org/10.1088/1361-6560/aeb598}
}RIS
TY - JOUR TI - High-fidelity 3D geometric reconstruction of pelvic organs from MRI: a hybrid deep learning and iterative optimization approach AU - Hui Wang AU - Xiaowei Li AU - Chenxin Zhang AU - Yifan Feng AU - Jianwei Zuo AU - Yumeng Tang AU - Xiuli Sun AU - Jianliu Wang AU - Bing Xie AU - Jiajia Luo PY - 2026 JO - Physics in Medicine & Biology DO - 10.1088/1361-6560/aeb598 UR - https://doi.org/10.1088/1361-6560/aeb598 ER -
APA
Wang, H., Li, X., Zhang, C., Feng, Y., Zuo, J., Tang, Y., Sun, X., Wang, J., Xie, B., & Luo, J. (2026). High-fidelity 3D geometric reconstruction of pelvic organs from MRI: a hybrid deep learning and iterative optimization approach. Physics in Medicine & Biology. https://doi.org/10.1088/1361-6560/aeb598
Source records
- crossref · retrieved 2026-11-10T10:24:19.323Z