用高斯表示实现跨模态脊柱图像高效合成
UniSpine-GS: An Efficient Physics-Aware Gaussian Framework for Cross-Modality Multi-view Spine Image Synthesis
- 通过几何感知高斯表示避免显式重建，保持多视角解剖一致性
- 在CTSpine3D和新构建的FeSpine3D数据集上全面超越现有方法
- 适合需要低成本多模态医学图像合成的研究者与临床开发者
脊柱疾病诊断常依赖3D成像技术，但高昂的3D成像设备成本及不同成像模态间的物理差异限制了模型的泛化能力。为此，我们提出UniSpine-GS，一种高效且物理感知的高斯框架，用于多视角脊柱图像的新视角投影渲染。该方法不进行显式3D重建，而是学习一种几何感知的高斯表示，确保不同视角间的解剖一致性。我们引入结构引导损失重加权策略（SPWM），提升边界保真度与局部细节表现。在CTSpine3D数据集和新构建的3D胎儿超声数据集FeSpine3D上评估，结果表明UniSpine-GS在所有指标上均显著优于现有方法，为统一多视角医学成像提供了实用且低成本的解决方案。代码已公开于https://github.com/orangeisland66/UniSpine-GS。
原文摘要 · Abstract (English)
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.
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