PyRadiomics-cuda: 3D features extraction from medical images for HPC using GPU acceleration
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915807656673280 |
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| author | Lisowski, Jakub Tyrakowski, Piotr Zyguła, Szymon Kaczmarski, Krzysztof |
| author_facet | Lisowski, Jakub Tyrakowski, Piotr Zyguła, Szymon Kaczmarski, Krzysztof |
| contents | PyRadiomics-cuda is a GPU-accelerated extension of the PyRadiomics library, designed to address the computational challenges of extracting three-dimensional shape features from medical images. By offloading key geometric computations to GPU hardware it dramatically reduces processing times for large volumetric datasets. The system maintains full compatibility with the original PyRadiomics API, enabling seamless integration into existing AI workflows without code modifications. This transparent acceleration facilitates efficient, scalable radiomics analysis, supporting rapid feature extraction essential for high-throughput AI pipeline. Tests performed on a typical computational cluster, budget and home devices prove usefulness in all scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02894 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PyRadiomics-cuda: 3D features extraction from medical images for HPC using GPU acceleration Lisowski, Jakub Tyrakowski, Piotr Zyguła, Szymon Kaczmarski, Krzysztof Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition PyRadiomics-cuda is a GPU-accelerated extension of the PyRadiomics library, designed to address the computational challenges of extracting three-dimensional shape features from medical images. By offloading key geometric computations to GPU hardware it dramatically reduces processing times for large volumetric datasets. The system maintains full compatibility with the original PyRadiomics API, enabling seamless integration into existing AI workflows without code modifications. This transparent acceleration facilitates efficient, scalable radiomics analysis, supporting rapid feature extraction essential for high-throughput AI pipeline. Tests performed on a typical computational cluster, budget and home devices prove usefulness in all scenarios. |
| title | PyRadiomics-cuda: 3D features extraction from medical images for HPC using GPU acceleration |
| topic | Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.02894 |