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

Publication

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

Journal Paper/Review - Dec 9, 2019

McKinley Richard, Wiest Roland, Chan Andrew, Salmen Anke, Reyes Mauricio, Muhlau Mark, Eichinger Paul, Christoph Berger, Wiestler Benedikt, Weisstanner Christian, Verma Rajeev, Rummel Christian, Muri Raphaela, Friedli Christoph, Fischer Tim, Aschwanden Fabian, Grunder Lorenz, Wepfer Rik, Wagner Franca

Citation

McKinley R, Wiest R, Chan A, Salmen A, Reyes M, Muhlau M, Eichinger P, Christoph B, Wiestler B, Weisstanner C, Verma R, Rummel C, Muri R, Friedli C, Fischer T, Aschwanden F, Grunder L, Wepfer R, Wagner F. Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. Neuroimage Clin 2019; 25:102104.

Type

Journal Paper/Review (English)

Journal

Neuroimage Clin 2019; 25

Publication Date

Dec 9, 2019

Issn Electronic

2213-1582

Pages

102104

Brief description/objective

The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper, we explore the ability of a deep learning segmentation classifier to separate stable from progressive patients by lesion volume and lesion count, and find that neither measure provides a good separation. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable time-points with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on two external datasets confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracies of 75 % and 85 % in separating stable and progressive time-points.