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

Abstract：

: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.

journal_name

Neuroimage Clin

journal_title

NeuroImage. Clinical

authors

McKinley R,Wepfer R,Grunder L,Aschwanden F,Fischer T,Friedli C,Muri R,Rummel C,Verma R,Weisstanner C,Wiestler B,Berger C,Eichinger P,Muhlau M,Reyes M,Salmen A,Chan A,Wiest R,Wagner F

doi

10.1016/j.nicl.2019.102104

subject

Has Abstract

pub_date

2020-01-01 00:00:00

pages

102104

issn

2213-1582

pii

S2213-1582(19)30451-6

journal_volume

25

pub_type

杂志文章

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