Chupetlovska Kalina, Akinci D'Antonoli Tugba, Bodalal Zuhir, Abdelatty Mohamed A, Erenstein Hendrik, Santinha João, Huisman Merel, Visser Jacob J, Trebeschi Stefano, Groot Lipman Kevin B W
Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
Eur Radiol. 2025 May 22. doi: 10.1007/s00330-025-11621-1.
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.
高质量的分割对于人工智能驱动的放射学研究和临床实践至关重要,并且在未来有可能发挥更突出的作用。随着医学成像技术的进步,准确分割解剖结构和病理结构越来越多地用于获取定量数据和有价值的见解。分割和容积分析能够实现更精确的诊断、治疗规划和患者监测。这些指南旨在提高分割的准确性和一致性,以便在研究和临床环境中做出更好的决策。提供了关于规划和组织的实用建议,重点关注临床团队之间的质量、精度和沟通。此外,还讨论了改进放射学和放射肿瘤学中分割实践的技巧和策略,以及需要避免的潜在陷阱。要点:随着人工智能的不断发展,容积分析将更深入地融入临床实践,这使得放射科医生了解其在诊断和治疗规划中的应用至关重要。对于像分割和容积分析这样的技术主题,严重缺乏专门为放射科医生量身定制的实用指南和资源。为分割建立明确的规则和最佳实践可以简化临床环境中的容积评估,使其更易于管理,并为患者护理带来更准确的决策。