ESR Essentials: a step-by-step guide of segmentation for radiologists—practice recommendations by the European Society of Medical Imaging Informatics

  • Kalina Chupetlovska
  • , Tugba Akinci D’Antonoli
  • , Zuhir Bodalal
  • , Mohamed A. Abdelatty
  • , Hendrik Erenstein
  • , João Santinha
  • , Merel Huisman
  • , Jacob J. Visser
  • , Stefano Trebeschi
  • , Kevin B.W. Groot Lipman*
  • *Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    1 Downloads (Pure)

    Abstract

    Abstract: High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in the future. As medical imaging advances, accurately segmenting anatomical and pathological structures is increasingly used to obtain quantitative data and valuable insights. Segmentation and volumetric analysis could enable more precise diagnosis, treatment planning, and patient monitoring. These guidelines aim to improve segmentation accuracy and consistency, allowing for better decision-making in both research and clinical environments. Practical advice on planning and organization is provided, focusing on quality, precision, and communication among clinical teams. Additionally, tips and strategies for improving segmentation practices in radiology and radiation oncology are discussed, as are potential pitfalls to avoid. 

    Key Points: As AI continues to advance, volumetry will become more integrated into clinical practice, making it essential for radiologists to stay informed about its applications in diagnosis and treatment planning. There is a significant lack of practical guidelines and resources tailored specifically for radiologists on technical topics like segmentation and volumetric analysis. Establishing clear rules and best practices for segmentation can streamline volumetric assessment in clinical settings, making it easier to manage and leading to more accurate decision-making for patient care.

    Original languageEnglish
    Pages (from-to)6894–6904
    Number of pages11
    JournalEuropean Radiology
    Volume35
    Early online date22-May-2025
    DOIs
    Publication statusPublished - Nov-2025

    Keywords

    • Artificial intelligence
    • Imaging
    • Segmentation
    • Volumetry

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