用3D高斯表示法高效重建稀疏医学影像，保持结构完整与语义一致。
Medical Scene Reconstruction and Segmentation based on 3D Gaussian Representation
- 结合3D高斯与三平面表示，提升稀疏切片下的重建效率
- 在超声与MRI数据上实现高质量、解剖连贯的三维可视化
- 适合临床手术规划与疾病评估场景使用
医学图像的3D重建是医学影像分析与临床诊断的关键技术，为疾病评估和手术规划提供结构可视化支持。传统方法计算成本高，且在稀疏切片下易出现结构断裂与细节丢失，难以满足临床精度要求。为此，我们提出一种基于3D高斯与三平面表示的高效3D重建方法。该方法不仅保留了高斯表示在高效渲染与几何表达上的优势，还在稀疏切片条件下显著提升了结构连续性与语义一致性。在多模态医学数据集（如超声与MRI）上的实验表明，所提方法可在稀疏数据条件下生成高质量、解剖合理且语义稳定的医学图像，同时大幅提升重建效率。为医学图像的3D可视化与临床分析提供了高效可靠的全新方案。
原文摘要 · Abstract (English)
3D reconstruction of medical images is a key technology in medical image analysis and clinical diagnosis, providing structural visualization support for disease assessment and surgical planning. Traditional methods are computationally expensive and prone to structural discontinuities and loss of detail in sparse slices, making it difficult to meet clinical accuracy requirements.To address these challenges, we propose an efficient 3D reconstruction method based on 3D Gaussian and tri-plane representations. This method not only maintains the advantages of Gaussian representation in efficient rendering and geometric representation but also significantly enhances structural continuity and semantic consistency under sparse slicing conditions. Experimental results on multimodal medical datasets such as US and MRI show that our proposed method can generate high-quality, anatomically coherent, and semantically stable medical images under sparse data conditions, while significantly improving reconstruction efficiency. This provides an efficient and reliable new approach for 3D visualization and clinical analysis of medical images.
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