Deep Learning Framework for Automated MRI Planimetry in Multiple Sclerosis
Stephanie Mangesius1,2, Daniela Schiefeneder3, Matthias Schwab1
1Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria, i-med.ac.at.
Abstract:
Brain volume changes and infratentorial involvement are key predictors of disability in multiple sclerosis (MS) and can be assessed using magnetic resonance imaging (MRI) planimetry. Although MRI planimetry is less susceptible to methodological and patient-related confounders than volumetry, it currently depends on manual measurements by unblinded experts, an approach that is time-consuming and vulnerable to bias. In this study, we present a fully automated deep learning framework for deriving brainstem planimetric measurements from MRI. The pipeline integrates an automated midsagittal plane (MSP) detection algorithm with a convolutional neural network trained to perform the segmentations required for planimetry. The automated method shows strong agreement with manual measurements and remains robust across scanners and acquisition protocols. These findings suggest that the proposed framework enables reliable, reproducible, and scalable MRI planimetry, supporting objective assessment of disease progression and treatment response in patients with MS.
Insights
This study introduces an automated deep learning framework for magnetic resonance imaging (MRI) planimetry in multiple sclerosis (MS). The AI tool accurately measures brainstem changes, improving disability prediction and disease monitoring.
Area of Science:
- Neuroimaging
- Artificial Intelligence in Medicine
- Neurology
Background:
- Brain volume changes and infratentorial involvement are crucial for predicting disability in multiple sclerosis (MS).
- Magnetic resonance imaging (MRI) planimetry offers a robust method for assessing these changes, but traditional manual measurements are time-consuming and prone to bias.
- Current planimetry relies on manual, unblinded expert analysis, limiting its scalability and reproducibility.
Purpose of the Study:
- To develop and validate a fully automated deep learning framework for deriving brainstem planimetric measurements from MRI.
- To enhance the objectivity, reliability, and scalability of MRI planimetry for MS assessment.
- To support more accurate disease progression and treatment response monitoring in MS patients.
Main Methods:
- A deep learning pipeline was created, integrating automated midsagittal plane (MSP) detection.
- A convolutional neural network (CNN) was trained to perform segmentations essential for planimetry.
- The automated framework was validated against manual measurements across different MRI scanners and protocols.
Main Results:
- The automated framework demonstrated strong agreement with manual planimetric measurements.
- The method proved robust and consistent across various scanners and acquisition protocols.
- The deep learning approach successfully automated the complex segmentation tasks required for planimetry.
Conclusions:
- The proposed automated framework enables reliable, reproducible, and scalable MRI planimetry.
- This AI-driven approach can overcome limitations of manual measurements, reducing bias and time.
- The tool supports objective assessment of disease progression and treatment efficacy in multiple sclerosis.
