PySERA: Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918219132960768 |
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| author | Salmanpour, Mohammad R. Pouria, Amir Hossein Barichin, Sirwan Salehi, Yasaman Falahati, Sonya Shiri, Isaac Oveisi, Mehrdad Rahmim, Arman |
| author_facet | Salmanpour, Mohammad R. Pouria, Amir Hossein Barichin, Sirwan Salehi, Yasaman Falahati, Sonya Shiri, Isaac Oveisi, Mehrdad Rahmim, Arman |
| contents | Radiomics enables the extraction of quantitative biomarkers from medical images for precision modeling, but reproducibility and scalability remain limited due to heterogeneous software implementations and incomplete adherence to standards. Existing tools also lack unified support for deep learning based radiomics. To address these limitations, we introduce PySERA, an open source, Python native, standardized radiomics framework designed for automation, reproducibility, and seamless AI integration. PySERA reimplements the MATLAB based SERA platform within a modular, object oriented architecture and computes 557 features, including 487 Image Biomarker Standardization Initiative (IBSI) compliant features, 10 moment invariant descriptors, and 60 diagnostic features, together with deep learning radiomics embeddings from pretrained networks such as ResNet50, DenseNet121, and VGG16. The framework provides standardized preprocessing, including resampling, discretization, and normalization, multi format image input and output, adaptive memory management, and a parallel multicores extraction engine. PySERA integrates natively with major machine learning ecosystems including scikit learn, PyTorch, TensorFlow, MONAI and others. In IBSI benchmarks, PySERA achieved more than 94 percent reproducibility and outperformed PyRadiomics while closely matching MITK. Across eight public datasets, it achieved predictive accuracies ranging from 0.54 to 0.87, consistently exceeding PyRadiomics. PySERA unifies handcrafted and deep learning radiomics in a transparent, scalable, and extensible Python framework, establishing a robust foundation for reproducible, AI ready imaging research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PySERA: Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics Salmanpour, Mohammad R. Pouria, Amir Hossein Barichin, Sirwan Salehi, Yasaman Falahati, Sonya Shiri, Isaac Oveisi, Mehrdad Rahmim, Arman Medical Physics F.2.2; I.2.7 Radiomics enables the extraction of quantitative biomarkers from medical images for precision modeling, but reproducibility and scalability remain limited due to heterogeneous software implementations and incomplete adherence to standards. Existing tools also lack unified support for deep learning based radiomics. To address these limitations, we introduce PySERA, an open source, Python native, standardized radiomics framework designed for automation, reproducibility, and seamless AI integration. PySERA reimplements the MATLAB based SERA platform within a modular, object oriented architecture and computes 557 features, including 487 Image Biomarker Standardization Initiative (IBSI) compliant features, 10 moment invariant descriptors, and 60 diagnostic features, together with deep learning radiomics embeddings from pretrained networks such as ResNet50, DenseNet121, and VGG16. The framework provides standardized preprocessing, including resampling, discretization, and normalization, multi format image input and output, adaptive memory management, and a parallel multicores extraction engine. PySERA integrates natively with major machine learning ecosystems including scikit learn, PyTorch, TensorFlow, MONAI and others. In IBSI benchmarks, PySERA achieved more than 94 percent reproducibility and outperformed PyRadiomics while closely matching MITK. Across eight public datasets, it achieved predictive accuracies ranging from 0.54 to 0.87, consistently exceeding PyRadiomics. PySERA unifies handcrafted and deep learning radiomics in a transparent, scalable, and extensible Python framework, establishing a robust foundation for reproducible, AI ready imaging research. |
| title | PySERA: Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics |
| topic | Medical Physics F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2511.15963 |