Fundamentals of ESR: Comprehensive Segmentation Guide for Radiologists

Overview

This guide from the European Society of Medical Imaging Informatics outlines standardized protocols and quality measures essential for effective image segmentation in radiology. It emphasizes the importance of pre-planned protocols, adherence to DICOM standards, and quality control frameworks, especially when using AI-assisted segmentation.

Background

Segmentation in radiology involves classifying each voxel in an image to delineate anatomical structures or lesions, supporting tasks such as lesion detection, volumetric measurements, and treatment planning. Traditionally manual and time-consuming, segmentation is increasingly automated with AI, improving efficiency and accessibility. Radiologists and other imaging professionals require clear guidelines to ensure consistency, accuracy, and clinical relevance in segmentation practices.

Data Highlights

Key metrics for image quality assessment include signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Structural Similarity Index, which quantify image fidelity and suitability for segmentation tasks. AI segmentation performance is tracked using metrics like the dice similarity coefficient (DSC) and radiologist adjustment time to ensure clinical workflow efficiency.

Key Findings

• Segmentation protocols must be pre-planned with clear clinical goals, patient selection criteria, imaging standards, and software tools to ensure consistency and relevance.
• Adherence to the DICOM SEG object standard is recommended for radiological segmentations to maintain interoperability and data integrity.
• Quality control frameworks are essential for AI-assisted segmentation, monitoring both segmentation accuracy (e.g., DSC) and clinical workflow impact (e.g., adjustment time).
• High-quality imaging with appropriate slice thickness, planes, and sequences is critical; poor image quality or artifacts can significantly impair segmentation accuracy.
• Standardized checklists and guidelines (e.g., RIDGE, CLAIM, CLEAR) support reproducibility, transparency, and quality assessment in segmentation and radiomics research.
• Supplementing protocols with visual atlases, consensus guidelines, and structured training improves segmentation accuracy and reduces interreader variability.

Clinical Implications

Implementing standardized segmentation protocols and quality control measures enhances the accuracy and reproducibility of radiological segmentations, directly benefiting clinical decision-making and patient management. Radiologists should integrate these guidelines and leverage AI tools with appropriate oversight to optimize workflow efficiency and diagnostic precision.

Conclusion

Clear, standardized guidelines and quality frameworks are essential for effective segmentation in radiology, enabling improved clinical outcomes through accurate lesion characterization and treatment planning. Collaboration between medical and technical experts is key to developing robust protocols that meet clinical and research needs.

References

1. European Society of Medical Imaging Informatics -- Fundamentals of ESR: A Comprehensive Guide to Segmentation for Radiologists