Advancing Annotat3D with Harpia: A CUDA-Accelerated Library for Large-Scale Volumetric Data Segmentation

• Camila Machado de Araujo LNLS / CNPEM
• Egon P. B. S. Borges LNLS / CNPEM
• Ricardo Marcelo Canteiro Grangeiro LNLS / CNPEM
• Allan Pinto LNLS / CNPEM

Resumo

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 highperformance 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.

Palavras-chave: Image segmentation, Three-dimensional displays, Scalability, Microscopy, Memory management, Graphics processing units, X-ray tomography, Libraries, Resource management, Reliability