ESR Essentials: a step-by-step guide of segmentation for radiologists—practice recommendations by the European Society of Medical Imaging Informatics - Scorecard - MDSpire
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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
Clinical Scorecard: Fundamentals of ESR: A Comprehensive Guide to Segmentation for Radiologists—Recommendations from the European Society of Medical Imaging Informatics
At a Glance
Category
Detail
Condition
Radiological image segmentation for clinical and research applications
Key Mechanisms
Manual, semi-automated, and AI-assisted segmentation classifying voxels to delineate anatomical structures and lesions
Target Population
Radiologists and imaging professionals involved in segmentation tasks
Care Setting
Clinical and research imaging environments including radiology and radiotherapy
Key Highlights
Segmentation protocols require multidisciplinary input, clear goals, imaging standards, and quality control frameworks.
High-quality images with appropriate reconstruction parameters and artifact assessment are essential for accurate segmentation.
AI-assisted segmentation should be monitored using performance metrics like dice similarity coefficient and radiologist adjustment time.
Guideline-Based Recommendations
Diagnosis
Plan segmentation protocols in advance to address clinical questions and ensure consistency.
Use suitable slice thickness, imaging planes, phases, or sequences aligned with clinical needs.
Management
Adhere to DICOM standards, specifically using the DICOM SEG object for segmentations.
Employ quality control frameworks for AI-assisted segmentation to track segmentation and workflow performance.
Incorporate standardized checklists (e.g., RIDGE, CLAIM, CLEAR) to guide protocol development and evaluation.
Monitoring & Follow-up
Use objective image quality metrics such as signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Structural Similarity Index.
Document and assess artifacts systematically, using structured scales like Likert ratings.
Monitor AI model performance on real-world image quality variations to ensure robustness.
Risks
Low image quality or artifacts can compromise segmentation accuracy and volumetric analyses.
Excluding images should be reserved for cases where artifacts severely impair segmentation reliability.
Interreader variability can be reduced by implementing clear protocols, visual atlases, and structured training.
Patient & Prescribing Data
Patients undergoing imaging studies requiring segmentation for diagnosis, treatment planning, or monitoring.
Segmentation supports lesion detection, volumetric tumor measurements, growth rate assessments, surgical and radiotherapy planning, and body composition analysis.
Clinical Best Practices
Develop segmentation protocols collaboratively with medical and technical experts including iterative testing.
Supplement written protocols with visual atlases, consensus guidelines, and structured training to improve accuracy and consistency.
Select appropriate image reconstruction parameters (e.g., soft tissue kernels for CT) to optimize segmentation quality.
Implement quality checks and detailed documentation throughout the segmentation workflow.
Use AI tools with integrated quality control and performance tracking to enhance efficiency and reliability.
by 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
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