[Approaches to segment multiple-sclerosis lesions on conventional brain MRI]

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Radiology

Context:

• Multiple sclerosis (MS) diagnosis relies on clinical and imaging criteria, including lesion dissemination.
• Assessing treatment efficacy in MS clinical research requires monitoring lesion load evolution.
• Manual lesion delineation in MRI scans exhibits significant inter- and intra-expert variability.

Purpose:

• To present a comprehensive classification of state-of-the-art image processing systems for multiple sclerosis (MS) lesion segmentation.
• To highlight automated and semi-automated approaches that minimize variability in lesion detection.
• To provide an overview of current methodologies for precise MS lesion segmentation.

Summary:

• Current multiple sclerosis (MS) diagnostic criteria incorporate spatial and temporal dissemination of lesions observed in imaging.
• Precise segmentation of MS lesions is critical for accurate diagnosis and evaluating treatment response.
• Various semi-automated and automated image analysis techniques have been developed to improve the reliability of lesion segmentation.

Impact:

• Facilitates more consistent and reliable MS diagnosis through standardized lesion segmentation.
• Enables more accurate monitoring of disease progression and treatment effectiveness in clinical trials.
• Reduces subjectivity in MRI-based assessments, leading to improved patient management and research outcomes.