Search Results for author: Christian Weisstanner

Found 2 papers, 0 papers with code

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

no code implementations5 Apr 2019 Richard McKinley, Lorenz Grunder, Rik Wepfer, Fabian Aschwanden, Tim Fischer, Christoph Friedli, Raphaela Muri, Christian Rummel, Rajeev Verma, Christian Weisstanner, Mauricio Reyes, Anke Salmen, Andrew Chan, Roland Wiest, Franca Wagner

Instead, we propose a method for identifying lesion changes of high certainty, and establish on a dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0. 99), while changes in lesion volume are much less able to perform this separation (AUC = 0. 71).

Lesion Segmentation

Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks

no code implementations22 Jan 2019 Richard McKinley, Rik Wepfer, Fabian Aschwanden, Lorenz Grunder, Raphaela Muri, Christian Rummel, Rajeev Verma, Christian Weisstanner, Mauricio Reyes, Anke Salmen, Andrew Chan, Franca Wagner, Roland Wiest

We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN).

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