{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:50:28Z","timestamp":1791521428478,"version":"4.3.4"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u00a0mm and 8.96\u00a0mm 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1972153","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:27:02Z","timestamp":1791520022000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Boundary-aware geometric Mamba network for breast tumor segmentation in 3D MRI"],"prefix":"10.3389","volume":"16","author":[{"given":"Zhejun","family":"Kuang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Changchun University","place":["Changchun, Jilin, China"]},{"name":"Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement","place":["Changchun, Jilin, China"]},{"name":"Key Laboratory of 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