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Radiomics extracts quantitative features from medical images, offering biomarkers for diagnosis, prognosis, and evaluation of treatment response. Yet, its broader application in research is limited by the absence of standardized, end-to-end workflows for multimodal imaging. We present an open-source Python-based pipeline that allows for interactive studies and series selection, as well as automated conversion, segmentation, and quantitative analysis of positron emission tomography (PET) / computed tomography (CT) DICOM images. Leveraging widely adopted segmentation models for PET analysis and CT organ delineation, the pipeline computes key radiomics, producing structured outputs for analysis. Its modular design facilitates reproducible, scalable, and clinically relevant radiomics studies, addressing a critical gap in medical image analysis infrastructure. The code is available under: https://github.com/Clinical-Computational-Medical-Imaging/MUSIQ

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AI-based Automated Framework for Quantitative PET/CT Image Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Sonja Adomeit,
  • Lukas Förner,
  • Elisabeth Scheurer,
  • Jan Bäßler,
  • Elina Gastreich de Llanes,
  • Jonas Böhringer,
  • Ralph A. Bundschuh,
  • Constantin Lapa,
  • Kartikay Tehlan,
  • Thomas Wendler

摘要

Radiomics extracts quantitative features from medical images, offering biomarkers for diagnosis, prognosis, and evaluation of treatment response. Yet, its broader application in research is limited by the absence of standardized, end-to-end workflows for multimodal imaging. We present an open-source Python-based pipeline that allows for interactive studies and series selection, as well as automated conversion, segmentation, and quantitative analysis of positron emission tomography (PET) / computed tomography (CT) DICOM images. Leveraging widely adopted segmentation models for PET analysis and CT organ delineation, the pipeline computes key radiomics, producing structured outputs for analysis. Its modular design facilitates reproducible, scalable, and clinically relevant radiomics studies, addressing a critical gap in medical image analysis infrastructure. The code is available under: https://github.com/Clinical-Computational-Medical-Imaging/MUSIQ