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Abstract #4728

The deep learning lesion segmentation method nicMSlesions only needs one manually delineated subject to outperform commonly used unsupervised methods

Merlin M Weeda1, Iman Brouwer1, Marlieke L de Vos1, Myrte S de Vries1, Frederik Barkhof1,2, Petra JW Pouwels1, and Hugo Vrenken1

1Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC - location VUmc, Amsterdam, Netherlands, 2Institutes of Neurology and Healthcare Engineering UCL, London, United Kingdom

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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