Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstruction
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915769039716352 |
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| author | Zhang, Tongxu Wang, Bei |
| author_facet | Zhang, Tongxu Wang, Bei |
| contents | High-fidelity 3D anatomical reconstruction is a prerequisite for downstream clinical tasks such as preoperative planning, radiotherapy target delineation, and orthopedic implant design. We present Med-PU, a knowledge-driven framework that integrates volumetric medical image segmentation with point cloud upsampling for accurate pelvic shape reconstruction. Unlike landmark- or PCA-based statistical shape models, Med-PU learns an implicit anatomical prior directly from large-scale 3D shape data, enabling dense completion and refinement from sparse segmentation-derived point sets. The pipeline couples SAM-Med3D-based voxel segmentation, point extraction, deep upsampling, and surface reconstruction, yielding smooth and topologically consistent meshes. We evaluate Med-PU on pelvic CT datasets (MedShapePelvic for training and Pelvic1k for validation), benchmarking against state-of-the-art upsampling methods using comprehensive geometry and surface metrics. Med-PU consistently improves surface quality and anatomical fidelity while reducing artifacts, demonstrating robustness across input densities. Although validated on the pelvis, the approach is anatomy-agnostic and applicable to other skeletal regions and organs. These results suggest Med-PU as a practical, generalizable tool to bridge segmentation outputs and clinically usable 3D models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16716 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstruction Zhang, Tongxu Wang, Bei Computer Vision and Pattern Recognition High-fidelity 3D anatomical reconstruction is a prerequisite for downstream clinical tasks such as preoperative planning, radiotherapy target delineation, and orthopedic implant design. We present Med-PU, a knowledge-driven framework that integrates volumetric medical image segmentation with point cloud upsampling for accurate pelvic shape reconstruction. Unlike landmark- or PCA-based statistical shape models, Med-PU learns an implicit anatomical prior directly from large-scale 3D shape data, enabling dense completion and refinement from sparse segmentation-derived point sets. The pipeline couples SAM-Med3D-based voxel segmentation, point extraction, deep upsampling, and surface reconstruction, yielding smooth and topologically consistent meshes. We evaluate Med-PU on pelvic CT datasets (MedShapePelvic for training and Pelvic1k for validation), benchmarking against state-of-the-art upsampling methods using comprehensive geometry and surface metrics. Med-PU consistently improves surface quality and anatomical fidelity while reducing artifacts, demonstrating robustness across input densities. Although validated on the pelvis, the approach is anatomy-agnostic and applicable to other skeletal regions and organs. These results suggest Med-PU as a practical, generalizable tool to bridge segmentation outputs and clinically usable 3D models. |
| title | Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.16716 |