High-Precision Computer-Assisted Segmentation of Multispectral MRI Data Sets in Patients with Multiple Sclerosis by a Flexible Machine Learning Image Analysis Approach

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Abstract

Automatic brain segmentation is an issue of specific clinical relevance in both diagnosis and therapy control of patients with demyelinating diseases such as Multiple Sclerosis (MS). We present a complete system for high-precision computer-assisted image analysis of multispectral MRI data based on a flexible machine learning approach. Careful quality evaluation shows that the system outperforms conventional threshold-based techniques w.r.t. inter-observer agreement levels for the quantification of relevant clinical parameters, such as white matter lesion load and brain parenchyma volume.