Related Experiment Video
Updated: Jun 5, 2026
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
PySERA: Open-source standardized python library for automated, scalable, and reproducible handcrafted and deep
Mohammad R Salmanpour1, Amir Hossein Pouria2, Sirwan Barichin2
1Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada.
Background:
Radiomics analyses extract quantitative biomarkers from medical images for precision modeling, yet reproducibility and scalability remain limited by heterogeneous and limited implementations. Existing tools support only partial standards and lack integration with deep learning (DL) radiomics. To address these gaps, we developed PySERA, an open-source, Python-native, standardized radiomics framework designed for automation, reproducibility, and AI integration.
Methods:
PySERA re-implements MATLAB-based SERA (standardized environment for radiomics analysis) in a modular, object-oriented Python architecture. It computes 557 features, including 487 features compliant with the Image Biomarker Standardization Initiative (IBSI) and 10 moment-invariant descriptors, as well as 60 additional diagnostic features, along with DL radiomics embeddings from pre-trained DL: ResNet50 (2048 features) DL radiomics features), DenseNet121 (1024), and VGG16 (512). It includes standardized preprocessing (resampling, discretization, normalization), multi-format I/O (DICOM, NIfTI, NRRD), adaptive memory handling, and a parallel multi-core engine for scalable feature extraction. PySERA integrates directly with libraries: scikit-learn/PyTorch/TensorFlow/MONAI, and others for downstream machine learning applications.
Results:
PySERA demonstrated >94% IBSI reproducibility, closely matching MITK and substantially outperforming PyRadiomics against the 487 IBSI-compliant feature reference set. Across 8 public datasets, PySERA achieved accuracies of 0.43-0.84, exceeding PyRadiomics for outcome prediction tasks. Benchmarking showed efficient processing (including added higher-order features not implemented in other software): 583 seconds (305 MB) for 166 features, and 2325 seconds (491 MB) for full extraction, with deterministic outputs across platforms.
Conclusions:
By uniting standardized handcrafted/DL radiomics in a scalable, transparent, and Python-integrable framework, PySERA establishes a reproducible and extensible foundation for next-generation, AI-ready precision imaging research.