发表机构
Karlsruhe Institute of Technology; HIDSS4Health – Helmholtz Information and Data Science School for Health; Scientific Center for Computing, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院; HIDSS4Health——赫尔姆霍兹健康信息数据科学学院; 卡尔斯鲁厄理工学院计算科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Volcanite是一个开源框架，在商品硬件上直接渲染TB级分割体数据，无需网格化或分布式处理，支持半万亿体素以100fps交互探索，加速连接组学等领域的发现与验证。
AI 中文摘要
现代成像技术产生TB级的分割体数据，为每个体素分配一个对象标签。这些分类性、边界敏感且标签丰富的数据支撑着连接组学和其他成像驱动领域，但其规模往往迫使人们通过切片、近似网格或分布式工作流进行解释，这些方式会模糊空间上下文和体素级缺陷。在此，我们展示了此类体数据可以在商品硬件上直接探索，借助Volcanite——一个用于密集分割渲染的开源框架。结合压缩感知数据处理、Vulkan GPU后端和分割专用渲染，Volcanite实现了低延迟探索，无需网格化或分布式基础设施，包括针对未发表、敏感或专有数据。在跨领域数据集中，它保留体素标签，添加阴影和全局光照，并以每秒100帧的速度渲染多达半万亿体素。通过用直接检查取代冗长的预处理，Volcanite将假设生成与数据准备解耦，将太字节级标签场转化为用于发现、验证和跨领域空间分析的交互式证据。
英文摘要
Modern imaging produces terabyte-scale segmentation volumes, assigning each voxel an object label. These categorical, boundary-sensitive and label-rich data underpin connectomics and other imaging-driven fields, yet their scale often forces interpretation through slices, approximate meshes or distributed workflows that obscure spatial context and voxel-level defects. Here we show that such volumes can be explored directly on commodity hardware with Volcanite, an open-source framework for dense-segmentation rendering. Combining compression-aware data handling, a Vulkan GPU backend and segmentation-specific rendering, Volcanite enables low-latency exploration without meshing or distributed infrastructure, including for unpublished, sensitive or proprietary data. Across multi-domain datasets, it preserves voxel labels, adds shadows and global illumination, and renders up to half a trillion voxels at 100 frames per second. By replacing lengthy preprocessing with direct inspection, Volcanite decouples hypothesis generation from data preparation and turns teravoxel label fields into interactive evidence for discovery, validation and cross-domain spatial analysis
