MBRCNet: Multi-view Breast Reconstruction and Classification Network
| dc.contributor.author | Pang, Yan | en_US |
| dc.contributor.author | Quiñones, Rubi | en_US |
| dc.contributor.editor | Gerrits, Tim | en_US |
| dc.contributor.editor | Teschner, Matthias | en_US |
| dc.date.accessioned | 2026-04-21T13:54:47Z | |
| dc.date.available | 2026-04-21T13:54:47Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | High-fidelity 3D reconstruction of the human breast from multi-view RGB images remains challenging, particularly for low-texture anatomy under sparse-view constraints. Standard imaging methods such as Computed Tomography or Magnetic Resonance Imaging provide dense volumetric data but impose monetary costs and radiation risks that limit routine use. Reconstructing 3D geometry from a limited number of 2D views is challenging, as low-texture, non-rigid surfaces with few projections frequently lead to geometric collapse or loss of instance-specific detail. Few prior methods address breast reconstruction under sparse-view constraints while also supporting downstream morphological analysis. To overcome these limitations, we propose a flexible framework, MBRCNet, which combines multi-view feature fusion with dual 2D/3D supervision tailored to low-texture, non-rigid anatomy and supports downstream morphology classification from reconstructed shape representations. Experiments show that MBRCNet improves reconstruction fidelity over relevant baselines and that reconstructed 3D shapes provide promising features for exploratory morphological grouping for clinically meaningful classifications. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | Eurographics 2026 - Posters | |
| dc.identifier.doi | 10.2312/egp.20261008 | |
| dc.identifier.isbn | 978-3-03868-300-1 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 2 pages | |
| dc.identifier.uri | https://doi.org/10.2312/egp.20261008 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/egp20261008 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Reconstruction; Cluster analysis; Mesh models; Machine learning; Health informatics | |
| dc.subject | CCS Concepts | |
| dc.subject | Computing methodologies | |
| dc.subject | Reconstruction | |
| dc.subject | Cluster analysis | |
| dc.subject | Mesh models | |
| dc.subject | Machine learning | |
| dc.subject | Health informatics | |
| dc.title | MBRCNet: Multi-view Breast Reconstruction and Classification Network | en_US |