发表机构
Indraprastha Institute of Information Technology Delhi（德里英迪拉甘地信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对医学图像可视化计算成本高的问题，提出基于高斯的体表示，用蒙特卡罗体估计和课程学习策略优化，能从稀疏监督中学习解剖细节与纹理，降低计算成本，支持高效渲染多模态医学数据集，实现高帧率和高压缩比。
AI 中文摘要
医学图像可视化需要体积渲染算法，在保持高渲染速度的同时保留解剖保真度。为解决大型体数据集的高计算成本，我们提出一种基于高斯的体表示，用于高效可视化密集医学体积，且不损害结构和辐射细节。我们使用蒙特卡罗体估计优化该表示，通过课程学习策略在训练中逐步纳入基于切片的采样。稀疏体素样本提供早期全局覆盖，切片样本捕获空间相关区域。这使得高斯表示能从稀疏监督中学习各种结构的解剖细节和相应纹理，同时显著降低与密集体素处理相关的计算成本。该表示支持基于切片的渲染方法，如剪切变形体渲染，能高效可视化包括MRI和冷冻切片体积在内的多模态医学数据集，同时保留解剖结构。使用稀疏监督，我们的方法实现了高达43.86 FPS的渲染，压缩比为11.31:1。
英文摘要
Medical image visualization requires volumetric rendering algorithms that preserve anatomical fidelity while maintaining high rendering speeds. To address the high computational cost of large volumetric datasets, we propose a Gaussian-based volumetric representation for efficient visualization of dense medical volumes without compromising structural and radiometric details. We optimize the proposed representation using Monte Carlo volumetric estimation, which enables training on a highly sparse subset of voxels while maintaining consistency with the dense volumetric objective. In addition, we introduce a curriculum learning strategy that progressively incorporates structured slice-based sampling during training. Sparse voxel samples provide an early global coverage of the volume, while slice samples capture spatially correlated regions that aid geometric structure and texture continuity. This combination enables the Gaussian representation to learn anatomical details of various structures and corresponding textures from sparse supervision while significantly reducing the computational cost associated with dense voxel processing. The learned representation supports slice-based rendering methods such as shear-warp volume rendering, enabling efficient visualization of multimodal medical datasets including MRI and Cryosection volumes while preserving anatomical structures. Using sparse supervision, our method achieves up to 43.86 FPS rendering with a compression ratio of 11.31:1.
