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

• URL: http://arxiv.org/abs/2511.11890v1
• Date: Fri, 14 Nov 2025 21:45:02 GMT
• Title: Advancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation
• Authors: Camila Machado de Araujo, Egon P. B. S. Borges, Ricardo Marcelo Canteiro Grangeiro, Allan Pinto,
• Abstract summary: This work introduces new capabilities to Annotat3D through Harpia.<n>The library is designed to support scalable, interactive segmentation for large 3D datasets in high-performance computing.<n>The system's interactive, human-in-the-loop interface, combined with efficient GPU resource management, makes it particularly suitable for collaborative scientific imaging.
• Score: 0.1499944454332829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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