Point Cloud Upsampling as Statistical Shape Model for Pelvic

We propose a novel framework that integrates medical image segmentation and point cloud upsampling for accurate shape reconstruction of pelvic models. Using the SAM-Med3D model for segmentation and a point cloud upsampling network trained on the MedShapeNet dataset, our method transforms sparse medical imaging data into high-resolution 3D bone models. This framework leverages prior knowledge of anatomical shapes, achieving smoother and more complete reconstructions. Quantitative evaluations using metrics such as Chamfer Distance etc, demonstrate the effectiveness of the point cloud upsampling in pelvic model. Our approach offers potential applications in reconstructing other skeletal structures, providing a robust solution for medical image analysis and statistical shape modeling.