UniSpine-GS: An Efficient Physics-Aware Gaussian Framework for Cross-Modality Multi-view Spine Image Synthesis
2026-07-06 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created UniSpine-GS, a method that helps doctors see 3D images of spines from different angles without expensive equipment. Instead of building full 3D models, their system uses a special math approach called a Gaussian representation to keep the spine's shape accurate when viewed from new directions. They also developed a technique called SPWM to improve fine details around edges. Tests on two spine image datasets showed their method works better than others while being simpler and cheaper to use.
3D imagingspinal diseasesGaussian representationmulti-view imagingstructure-guided lossCT imagingultrasoundimage reconstructionanatomical consistency
Authors
Qiuhua Chen, Changning Yu, Na Huang, Chao Sun, Bo Du
Abstract
The diagnosis of spinal diseases is often assisted by 3D imaging techniques in clinical practice. However, precise 3D spinal assessment is limited by the high costs of 3D imaging hardware and the challenges posed by the physical differences between imaging modalities, which hinder the generalizability of models. To address these issues, we propose UniSpine-GS, an efficient, physics-aware Gaussian framework designed for novel-view projection rendering in multi-view spine imaging via a 3D-aware representation. Instead of performing explicit 3D reconstruction, our approach learns a geometry-aware Gaussian representation that ensures anatomical consistency across different views. We introduce SPWM, a structure-guided loss reweighting strategy to improve boundary fidelity and local details. We evaluate our method on the CTSpine3D dataset and a newly constructed 3D fetal ultrasound dataset, FeSpine3D. Our results demonstrate that UniSpine-GS significantly outperforms existing methods across all metrics, offering a practical and cost-effective solution for unified multi-view medical imaging. Our code is publicly available at https://github.com/orangeisland66/UniSpine-GS.