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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
02:09

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.

Computer Methods and Programs in Biomedicine
|June 3, 2026
PubMed
Summary

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PySERA is a new Python framework for radiomics analysis, improving reproducibility and integrating deep learning features. It offers a scalable and standardized solution for AI-ready precision imaging research.

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence

Background:

  • Radiomics analysis faces challenges in reproducibility and scalability due to fragmented implementations.
  • Existing tools often lack integration with deep learning (DL) radiomics and support only partial standards.

Purpose of the Study:

  • To develop PySERA, an open-source, Python-native, standardized radiomics framework.
  • To enhance automation, reproducibility, and AI integration in radiomics analysis.

Main Methods:

  • PySERA is a modular, object-oriented Python framework implementing standardized radiomics analysis.
  • It computes 557 features, including 487 IBSI-compliant features and DL radiomics embeddings (ResNet50, DenseNet121, VGG16).
  • Features standardized preprocessing, multi-format I/O, adaptive memory handling, and a parallel multi-core engine for scalable extraction, integrating with ML libraries.
Keywords:
Deep radiomics featuresDiagnosticMedical ImagePrognosticQuantitative analysisStandardized handcrafted radiomics feature

Related Experiment Videos

Last Updated: Jun 5, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Main Results:

  • PySERA demonstrated >94% IBSI reproducibility, outperforming PyRadiomics.
  • Achieved accuracies of 0.43-0.84 across 8 public datasets for outcome prediction.
  • Efficient processing with deterministic outputs across platforms, handling both handcrafted and DL features.

Conclusions:

  • PySERA provides a reproducible and extensible foundation for AI-ready precision imaging research.
  • It unifies standardized handcrafted and DL radiomics in a scalable, transparent, Python-integrable framework.