Evidence map›Paper›PMID 41801591›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Gia-Net: geometry-informed attention network for 3D point cloud registration in liver surgery.

Xiaoyue Liu, Tian Xu, Ziyi Jin, Xiang Deng, Tianyuan Gan, Zuoming Fu, Chongan Zhang, Peng Wang, Hong Zhang, Liangliang Yu and 3 more

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Xiaoyue Liu *College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Tian Xu *College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Ziyi JinCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Xiang DengCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Tianyuan GanCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Zuoming FuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Chongan ZhangCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Peng WangCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Hong ZhangCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Liangliang YuDepartment of General Surgery, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, 310016, China.
Xiao LiangDepartment of General Surgery, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, 310016, China.
Liping QinZhejiang Institute of Medical Device Supervision and Testing, Hangzhou, 310018, China.
Xuesong YeCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China. yexuesong@zju.edu.cn.

Funding

Key Research and Development Plan of Zhejiang Province Grant Nos. 2022C03086National Key R&D·Program·of.China Grant Nos. 2022YFB4700803
6 · The paper itself

Abstract

purpose3D rigid registration in laparoscopic liver surgery (LLS) is crucial for augmented reality navigation. However, liver surface registration faces several challenges, including the smooth surface with few distinctive textures, density variations between preoperative and intraoperative point clouds, and partial visibility of intraoperative surfaces.

methodsTo address these issues, we propose a geometry-informed attention network (GIA-Net) that leverages surface curvature and normal vectors to improve feature discrimination. GIA-Net includes two key components: a Geometry Transformer that integrates geometric priors for more distinctive feature representation, and a Representative Point Selector that balances local density between point clouds to enhance registration robustness and accuracy.

resultsExperiments on the MedShapeNet, 3Dircadb, and DePoLL datasets show that GIA-Net achieves lower transformation errors than existing rigid registration methods. On the DePoLL dataset, our method reduces the mean target registration error (TRE) by 2.23 cm compared to the baseline.

conclusionsLeveraging geometric information significantly enhances rigid registration performance, providing a reliable foundation for subsequent nonrigid refinement in liver surgery navigation.

Indexed as

HepatectomyImaging, Three-DimensionalLaparoscopyLiverNeural Networks, ComputerSurgery, Computer-AssistedAlgorithmsHumans3D rigid registrationGeometric featuresLiver surfaceRepresentative point selector

Identifiers

PMID41801591

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.