{
  "abstract": "Introduction: Accurate breast tumor segmentation in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is important for reliable diagnosis and treatment planning. However, complex tumor morphology, diverse spatial structures, and indistinct boundaries remain challenging for existing segmentation methods. Convolutional neural network-based methods have limited ability to model long-range dependencies, while Transformer-based methods often introduce substantial computational overhead. Although Mamba-based approaches provide an efficient alternative, conventional scanning strategies may suffer from directional bias and insufficiently capture three-dimensional structural variations.\n\nMethods: We propose a Boundary-Aware Geometric Mamba Network (BAGMNet) for three-dimensional breast tumor segmentation. BAGMNet establishes a structural representation learning framework that combines geometric modeling, multi-directional state-space propagation, and boundary-aware feature refinement. The Geometric Mamba Encoder constructs complementary spatial representations through multiple geometric transformations, while the Multi-directional Selective State Space module enhances three-dimensional dependency modeling and alleviates the directional bias of conventional Mamba scanning. The Gated State-space Fusion and Adaptive Skip Recalibration modules further integrate local structural details with global contextual information and enhance boundary-related representations during feature propagation.\n\nResults: Extensive experiments were conducted on the ISPY1 and Yunnan breast MRI datasets. BAGMNet achieved Dice scores of 70.34% and 79.14%, with average surface distances of 9.79 mm and 8.96 mm on the ISPY1 and Yunnan datasets, respectively. Compared with representative convolutional neural network-, Transformer-, and Mamba-based methods, BAGMNet achieved competitive performance in both regional segmentation accuracy and boundary localization.",
  "abstract_shown": true,
  "affiliations": [
    "College of Computer Science and Technology, Changchun University , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhejun Kuang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yuxuan Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Dawen Sun"
    },
    {
      "affs": [
        0
      ],
      "name": "Jian Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1jbm6rnbed7y5.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1972153",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690640/?tool=EBI",
  "id": "1jbm6rnbed7y5",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13690640",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7221116512",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690640/pdf/?tool=EBI",
  "pmcid": "PMC13690640",
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/1jbm6rnbed7y5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:59Z",
      "native_id": "PMC:PMC13690640",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13690640"
    },
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1972153",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1972153/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2234-943X",
      "subfield": "oncology"
    }
  ],
  "title": "Boundary-aware geometric Mamba network for breast tumor segmentation in 3D MRI",
  "title_text": "Boundary-aware geometric Mamba network for breast tumor segmentation in 3D MRI",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:13Z",
  "url": "https://research.draconitic.com/paper/1jbm6rnbed7y5",
  "venue": "Frontiers in oncology",
  "versions": []
}