TY - JOUR
T1 - ESR Essentials
T2 - a step-by-step guide of segmentation for radiologists—practice recommendations by the European Society of Medical Imaging Informatics
AU - Chupetlovska, Kalina
AU - Akinci D’Antonoli, Tugba
AU - Bodalal, Zuhir
AU - Abdelatty, Mohamed A.
AU - Erenstein, Hendrik
AU - Santinha, João
AU - Huisman, Merel
AU - Visser, Jacob J.
AU - Trebeschi, Stefano
AU - Groot Lipman, Kevin B.W.
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/11
Y1 - 2025/11
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Imaging
KW - Segmentation
KW - Volumetry
UR - https://www.scopus.com/pages/publications/105006571484
U2 - 10.1007/s00330-025-11621-1
DO - 10.1007/s00330-025-11621-1
M3 - Review article
C2 - 40402288
AN - SCOPUS:105006571484
SN - 0938-7994
VL - 35
SP - 6894
EP - 6904
JO - European Radiology
JF - European Radiology
ER -