Advancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909903599173632 |
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| author | de Araujo, Camila Machado Borges, Egon P. B. S. Grangeiro, Ricardo Marcelo Canteiro Pinto, Allan |
| author_facet | de Araujo, Camila Machado Borges, Egon P. B. S. Grangeiro, Ricardo Marcelo Canteiro Pinto, Allan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation de Araujo, Camila Machado Borges, Egon P. B. S. Grangeiro, Ricardo Marcelo Canteiro Pinto, Allan Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing 68U10, 62H35, 68W10 I.4.3; I.4.6; I.4.7; I.5.3; I.5.4 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. |
| title | Advancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation |
| topic | Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing 68U10, 62H35, 68W10 I.4.3; I.4.6; I.4.7; I.5.3; I.5.4 |
| url | https://arxiv.org/abs/2511.11890 |