  Although the segmentation of brain structures in ultrasound helps initialize
image based registration, assist brain shift compensation, and provides
interventional decision support, the task of segmenting grey and white matter
in cranial ultrasound is very challenging and has not been addressed yet. We
train a multi-scale fully convolutional neural network simultaneously for two
classes in order to segment real clinical 3D ultrasound data. Parallel pathways
working at different levels of resolution account for high frequency speckle
noise and global 3D image features. To ensure reproducibility, the publicly
available RESECT dataset is utilized for training and cross-validation. Due to
the absence of a ground truth, we train with weakly annotated label. We
implement label transfer from MRI to US, which is prone to a residual but
inevitable registration error. To further improve results, we perform transfer
learning using synthetic US data. The resulting method leads to excellent Dice
scores of 0.7080, 0.8402 and 0.9315 for grey matter, white matter and
background. Our proposed methodology sets an unparalleled standard for white
and grey matter segmentation in 3D intracranial ultrasound.
