Automatic lesion segmentation is important for measurements of atrophy and lesion load in subjects with multiple sclerosis (MS). Although supervised methods perform overall better than unsupervised methods, they are not widely used since they are more labor-intensive due to the need for great amounts of manual input. Our research showed increased performance of supervised methods over unsupervised methods. In addition, when using a deep learning based supervised method, training on only one subject already outperformed the commonly used unsupervised methods. We therefore recommend using deep learning lesion segmentation methods in MS research.
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