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Abstract #1417

Clinical Evaluation of a Fully Convolutional Neural Network for Automatic MS Lesion Segmentation on MRI

Amalie Monberg Hindsholm1, Claes Nøhr Ladefoged1, Flemming Littrup Andersen1, Stig Præstekjær Cramer1, Liselotte Højgaard1, and Ulrich Lindberg1
1Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet University, Copenhagen, Denmark

Automatic segmentation of MRI-visible multiple sclerosis (MS) lesions could potentially reduce assessment time and inter- and intra-rater variability. Recently, automatic methods using deep convolutional neural networks (CNN) have obtained great results in image segmentation. This work implements a state-of-the-art 2D CNN-based segmentation method from literature and extends and recalibrates it to a local MS dataset of 91 patients. A clinical evaluation is performed on an independent MS dataset of 53 patients, where 94% of predicted segmentation masks were deemed valuable for clinical use.

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