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

Publication date: Available online 9 December 2019Source: NeuroImage: ClinicalAuthor(s): Richard McKinley, Rik Wepfer, Lorenz Grunder, Fabian Aschwanden, Tim Fischer, Christoph Friedli, Raphaela Muri, Christian Rummel, Rajeev Verma, Christian Weisstanner, Benedikt Wiestler, Christoph Berger, Paul Eichinger, Mark Muehlau, Mauricio Reyes, Anke Salmen, Andrew Chan, Roland Wiest, Franca WagnerAbstractThe 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 ...
Source: NeuroImage: Clinical - Category: Radiology Source Type: research