Automatic segmentation could substantially simplify the procedure. The method first uses a CNN to extract the intracranial volume. Then, another CNN with the same architecture is employed to segment the volume into seven brain tissue classes.
MR images of fetuses allow clinicians to detect brain abnormalities in an
early stage of development. The cornerstone of volumetric and morphologic
analysis in fetal MRI is segmentation of the fetal brain into different tissue
classes. Manual segmentation is cumbersome and time consuming, hence automatic
segmentation could substantially simplify the procedure. However, automatic
brain tissue segmentation in these scans is challenging owing to artifacts
including intensity inhomogeneity, caused in particular by spontaneous fetal
movements during the scan. Unlike methods that estimate the bias field to
remove intensity inhomogeneity as a preprocessing step to segmentation, we
propose to perform segmentation using a convolutional neural network that
exploits images with synthetically introduced intensity inhomogeneity as data
augmentation. The method first uses a CNN to extract the intracranial volume.
Thereafter, another CNN with the same architecture is employed to segment the
extracted volume into seven brain tissue classes: cerebellum, basal ganglia and
thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical
gray matter and extracerebral cerebrospinal fluid. To make the method
applicable to slices showing intensity inhomogeneity artifacts, the training
data was augmented by applying a combination of linear gradients with random
offsets and orientations to image slices without artifacts.