用CUDA加速3D体数据分割，支持超大规模数据实时交互处理。
Advancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation
- 基于CUDA设计，支持分块执行与内存精准控制
- 处理速度和内存效率优于cuCIM、scikit-image
- 适合科研团队在共享超算上协同进行3D图像分析
高分辨率体成像技术（如X射线断层扫描和高级显微镜）生成的数据集日益庞大，对现有工具的高效处理、分割和交互探索能力构成挑战。本文通过引入Harpia——一个基于CUDA的新型处理库，扩展了Annotat3D的功能，使其可在高性能计算（HPC）和远程访问环境中支持大规模3D数据的可扩展、交互式分割工作流。Harpia具备严格的内存控制、原生分块执行以及一系列GPU加速的滤波、标注与量化工具，可在单个GPU内存容量之外稳定运行。实验表明，相较于广泛使用的NVIDIA cuCIM和scikit-image框架，该系统在处理速度、内存效率和可扩展性方面均有显著提升。其交互式人机协同界面结合高效的GPU资源管理，特别适用于共享超算环境中的协作科学成像流程。
原文摘要 · Abstract (English)
High-resolution volumetric imaging techniques, such as X-ray tomography and advanced microscopy, generate increasingly large datasets that challenge existing tools for efficient processing, segmentation, and interactive exploration. This work introduces new capabilities to Annotat3D through Harpia, a new CUDA-based processing library designed to support scalable, interactive segmentation workflows for large 3D datasets in high-performance computing (HPC) and remote-access environments. Harpia features strict memory control, native chunked execution, and a suite of GPU-accelerated filtering, annotation, and quantification tools, enabling reliable operation on datasets exceeding single-GPU memory capacity. Experimental results demonstrate significant improvements in processing speed, memory efficiency, and scalability compared to widely used frameworks such as NVIDIA cuCIM and scikit-image. The system's interactive, human-in-the-loop interface, combined with efficient GPU resource management, makes it particularly suitable for collaborative scientific imaging workflows in shared HPC infrastructures.
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