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Abstract <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p><i>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</i>.</p> </ItemContent> <ItemContent> <p><i>There is a significant lack of practical guidelines and resources tailored specifically for radiologists on technical topics like segmentation and volumetric analysis</i>.</p> </ItemContent> <ItemContent> <p><i>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</i>.</p> </ItemContent> </UnorderedList></p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月22日

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  • 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

摘要

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.