  Recently, accurate mandible segmentation in CT scans based on deep learning
methods has attracted much attention. However, there still exist two major
challenges, namely, metal artifacts among mandibles and large variations in
shape or size among individuals. To address these two challenges, we propose a
recurrent segmentation convolutional neural network (RSegCNN) that embeds
segmentation convolutional neural network (SegCNN) into the recurrent neural
network (RNN) for robust and accurate segmentation of the mandible. Such a
design of the system takes into account the similarity and continuity of the
mandible shapes captured in adjacent image slices in CT scans. The RSegCNN
infers the mandible information based on the recurrent structure with the
embedded encoder-decoder segmentation (SegCNN) components. The recurrent
structure guides the system to exploit relevant and important information from
adjacent slices, while the SegCNN component focuses on the mandible shapes from
a single CT slice. We conducted extensive experiments to evaluate the proposed
RSegCNN on two head and neck CT datasets. The experimental results show that
the RSegCNN is significantly better than the state-of-the-art models for
accurate mandible segmentation.
