Segmentation of both white matter lesions and deep grey matter structures is
an important task in the quantification of magnetic resonance imaging in
multiple sclerosis. Typically these tasks are performed separately: in this
paper we present a single segmentation solution based on convolutional neural
networks (CNNs) for providing fast, reliable segmentations of multimodal
magnetic resonance images into lesion classes and normal-appearing grey- and
white-matter structures. We show substantial, statistically significant
improvements in both Dice coefficient and in lesion-wise specificity and
sensitivity, compared to previous approaches, and agreement with individual
human raters in the range of human inter-rater variability. The method is
trained on data gathered from a single centre: nonetheless, it performs well on
data from centres, scanners and field-strengths not represented in the training
dataset. A retrospective study found that the classifier successfully
identified lesions missed by the human raters.
Lesion labels were provided by human raters, while weak labels for other
brain structures (including CSF, cortical grey matter, cortical white matter,
cerebellum, amygdala, hippocampus, subcortical GM structures and choroid
plexus) were provided by Freesurfer 5.3. The segmentations of these structures
compared well, not only with Freesurfer 5.3, but also with FSL-First and
Freesurfer 6.0