Abstract #1879

Automatic deep learning segmentation of grey and white matter lesions in 7T MRI data

Frederik Luca Sandig^1,2, Julian Emmerich^3,4, Edris El-Sanosy^1,2, Mark Ladd^1,2,3, Heinz-Peter Schlemmer¹, and Sina Straub³

¹Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ²Faculty of Medicine, Heidelberg University, Heidelberg, Germany, ³Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁴Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany

Multiple sclerosis is a chronic inflammatory disease characterized by demyelination. Magnetic resonance imaging (MRI) is an important method for diagnosis and prognosis predictions. The ongoing study presented here shows the use of deep learning algorithms for white and grey matter lesion segmentation in 7T MRI images. Results show high accuracy for patients with high lesion load. Furthermore, it is demonstrated that it is possible to train a neural net to find small cortical lesions, which can be used as a potential biomarker.

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