PySERA

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PySERA: Python-based Standardized Extraction for Radiomics Analysis

PySERA (Python-based Standardized Extraction for Radiomics Analysis), published in “PySERA“, is a comprehensive Python library for radiomics feature extraction from medical imaging data. It provides a simple, single-function API with built-in multiprocessing support, comprehensive report capabilities, and optimized performance through OOP architecture, RAM optimization, and CPU-efficient parallel processing. PySERA supports both traditional handcrafted radiomics (557 features including 487 IBSI-compliant, 60 diagnostic, and 10 moment-invariant features) and deep learning-based feature extraction using pre-trained models like ResNet50, VGG16, and DenseNet121.

PySERA: Python-based Standardized Extraction for Radiomics Analysis

IBSI Standardization

Both the script and library have been rigorously standardized based on the Image Biomarker Standardisation Initiative (IBSI) Standardization 1.0. PySERA returns IBSI-compliant feature values that match the reference standard, ensuring reproducibility and comparability across studies. The detailed evaluation and test cases can be found in the following link: IBSI_Evaluation Folder

Key Features

PySERA provides a single-function API that handles all radiomics processing:

import pysera
result = pysera.process_batch(
    image_input="image.nii.gz",
    mask_input="mask.nii.gz",
    output_path="./results"
)

All the complexity of multiprocessing, error & warning reports, file format handling, and feature extraction is handled automatically.

  • Single Function API: One function does everything – pysera.process_batch()
  • Multi-format Support: NIfTI, DICOM, NRRD, RTSTRUCT, NumPy arrays, and more
  • Automatic Multiprocessing: Built-in parallel processing for maximum performance
  • Comprehensive Report: Excel export functionality for detailed analysis
  • Extensive Features: 557 total radiomics features across multiple categories (morphological, statistical, texture, etc.) and dimensions (1st, 2D, 2.5D, 3D) including:
    • 487 IBSI-compliant features (standardized radiomics)
    • 60 diagnostic features (image quality and metadata)
    • 10 moment-invariant features (shape descriptors)
  • Medical Image Optimized: Designed for CT, MRI, PET, SPECT, X-Ray, Ultrasound, and other medical imaging modalities.
  • Dual Extraction Modes: Both traditional IBSI-compliant radiomics (557 features) and deep learning features (ResNet50, VGG16, DenseNet121)

Deep Learning Feature Extraction

PySERA supports advanced deep learning-based feature extraction alongside traditional radiomics, providing multiple pre-trained models for comprehensive feature representation. When using extraction_mode=”deep_feature”, the categories parameter is automatically handled by the deep learning model. Deep features are extracted in 3D dimension by default for comprehensive volumetric analysis. All deep learning features are extracted specifically from the ROI regions defined by the mask and model outputs provide complementary feature representations to traditional radiomics.

Available Deep Learning Models:

  • resnet50 – 2048 features: Residual Network with 50 layers, balanced performance and accuracy
  • vgg16 – 512 features: Visual Geometry Group with 16 layers, strong hierarchical feature representation
  • densenet121 – 1024 features: Dense Convolutional Network with 121 layers, efficient feature reuse

PySERA is also the central radiomics engine for our visualized radiomics software Radiuma, enabling users to create reproducible radiomics pipelines through a visual, user-friendly environment.

GitHub Page

PyPI Page

SlicerPySERA (3DSlicer extension)

YouTube Channel (Radiuma Platform)

Reference Publication (cite when using PySERA)

Contact Project Lead: Mohammad Salmanpour

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