  Automatic neonatal brain tissue segmentation in preterm born infants is a
prerequisite for evaluation of brain development. However, automatic
segmentation is often hampered by motion artifacts caused by infant head
movements during image acquisition. Methods have been developed to remove or
minimize these artifacts during image reconstruction using frequency domain
data. However, frequency domain data might not always be available. Hence, in
this study we propose a method for removing motion artifacts from the already
reconstructed MR scans. The method employs a generative adversarial network
trained with a cycle consistency loss to transform slices affected by motion
into slices without motion artifacts, and vice versa. In the experiments 40
T2-weighted coronal MR scans of preterm born infants imaged at 30 weeks
postmenstrual age were used. All images contained slices affected by motion
artifacts hampering automatic tissue segmentation. To evaluate whether
correction allows more accurate image segmentation, the images were segmented
into 8 tissue classes: cerebellum, myelinated white matter, basal ganglia and
thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical
gray matter, and extracerebral cerebrospinal fluid. Images corrected for motion
and corresponding segmentations were qualitatively evaluated using 5-point
Likert scale. Before the correction of motion artifacts, median image quality
and quality of corresponding automatic segmentations were assigned grade 2
(poor) and 3 (moderate), respectively. After correction of motion artifacts,
both improved to grades 3 and 4, respectively. The results indicate that
correction of motion artifacts in the image space using the proposed approach
allows accurate segmentation of brain tissue classes in slices affected by
motion artifacts.
