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MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation

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dc.contributor.authorLi, H.
dc.contributor.authorLi, X.
dc.contributor.authorWang, H.
dc.contributor.authorShi, J.
dc.contributor.authorChen, H.
dc.contributor.authorZhao, Y.
dc.contributor.authorDu, B.
dc.contributor.authorBarthelemy, J.
dc.contributor.authorKihara, D.
dc.contributor.authorShen, J.
dc.contributor.authorXu, M.
dc.date.issued2026
dc.description.abstractCryo-Electron Tomography (cryo-ET) is a cutting-edge 3D imaging technology that enables detailed examination of biological macromolecular structures at near-atomic resolution. Recent deep learning applications on cryo-ET, such as cryo-ET segmentation, have drawn widespread interest for their potential to improve particle alignment, classification, and other tasks. However, current methods heavily rely on convolutional architectures, which prioritize local information while neglecting the global structural information inherent in cryo-ET data. Transformer-based models, known for their large receptive field, have become the de-facto design for 2D vision tasks due to their ability to effectively capture global information. This approach is also well-suited for 3D tasks, given the complex nature of 3D objects. Based on this, we extend 2D vision transformers into 3D and propose a novel transformer-based framework for cryo-ET segmentation, named MVGFormer. MVGFormer introduces a multi-view perspective fusion transformer encoder, which captures rich global structural information from multiple perspectives using unique positional embeddings. To enhance contextual awareness, we design a parallel context encoder that builds a visual graph to guide attention. We further introduce two complementary 3D decoders: multi-level feature fusion (MF) and parallel atrous convolutions (P3DA), which together capture multi-scale structural cues for precise segmentation. Furthermore, we introduce a view-masked self-supervised learning strategy to reinforce the effectiveness of the multi-view design and improve the model’s representation capability. To our knowledge, MVGFormer is the first transformer-based model for cryo-ET segmentation. We empirically evaluate MVGFormer on six cryo-ET datasets across three different tasks. Extensive experimental results demonstrate its superiority over state-of-the-art 3D segmentation methods.
dc.description.statementofresponsibilityHaoran Li, Xingjian Li, Huan Wang, Jiahua Shi, Huaming Chen, Yizhou Zhao, Bo Du, Johan Barthelemy, Daisuke Kihara, Jun Shen, Min Xu
dc.identifier.citationKnowledge-Based Systems, 2026; 331:114810-1-114810-15
dc.identifier.doi10.1016/j.knosys.2025.114810
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.orcidChen, H. [0000-0001-5678-472X]
dc.identifier.urihttps://hdl.handle.net/2440/149246
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/IC220100028
dc.rights© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.source.urihttps://doi.org/10.1016/j.knosys.2025.114810
dc.subjectCryo-electron tomography; volumetric image segmentation; deep learning
dc.titleMVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation
dc.typeJournal article
pubs.publication-statusPublished

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