Abstract #4704

Segmentation of Multiple Sclerosis lesions using 7T multi-contrast MRI data

Anna Petrova^1,2, Assunta Dal-Bianco^2,3, Eva Niess¹, Nik Krajnc^2,3, Wolfgang Bogner¹, Günther Grabner⁴, Paul Rommer^2,3, and Stanislav Motyka¹

¹High Filed MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ²Department of Neurology, Medical University of Vienna, Vienna, Austria, ³Comprehensive Center for Clinical Neurosciences & Mental Health, Medical University of Vienna, Vienna, Austria, ⁴Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence

Motivation: The effective treatment of Multiple sclerosis (MS) requires reliable estimates of lesion load and hence precise lesion detection over time. However, current lesion load estimation is either qualitative or too time-consuming.

Goal(s): Our study automates MS lesion segmentation by training DeepMedic for application to 7T multi-contrast MRI data of MS patients.

Approach: Training with all four contrasts achieved the best results compared to Lesion Segmentation Tool (LST)—a conventional/non-deep-learning SPM-based MS lesions segmentation approach.

Results: Our study highlights potential for automating MS lesion detection/segmentation for 7T multi-contrast MRI data, underscoring the importance of accurate ground truth data and high-quality databases for improved detection accuracy.

Impact: The results of this research will impact the user-independent detection/segmentation of multiple sclerosis lesions, making manual assessment by clinicians obsolete and enable fully automated monitoring of lesions load as a quantitative radiological marker of disease progression.

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