  Segmentation of the fetal brain from stacks of motion-corrupted fetal MRI
slices is important for motion correction and high-resolution volume
reconstruction. Although Convolutional Neural Networks (CNNs) have been widely
used for automatic segmentation of the fetal brain, their results may still
benefit from interactive refinement for challenging slices. To improve the
efficiency of interactive refinement process, we propose an Uncertainty-Guided
Interactive Refinement (UGIR) framework. We first propose a grouped
convolution-based CNN to obtain multiple automatic segmentation predictions
with uncertainty estimation in a single forward pass, then guide the user to
provide interactions only in a subset of slices with the highest uncertainty. A
novel interactive level set method is also proposed to obtain a refined result
given the initial segmentation and user interactions. Experimental results show
that: (1) our proposed CNN obtains uncertainty estimation in real time which
correlates well with mis-segmentations, (2) the proposed interactive level set
is effective and efficient for refinement, (3) UGIR obtains accurate refinement
results with around 30% improvement of efficiency by using uncertainty to guide
user interactions. Our code is available online.
