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Recently, accurate mandible segmentation in CT scans based on deep learningmethods has attracted much attention. However, there still exist two majorchallenges, namely, metal artifacts among mandibles and large variations inshape or size among individuals. To address these two challenges, we propose arecurrent segmentation convolutional neural network (RSegCNN) that embedssegmentation convolutional neural network (SegCNN) into the recurrent neuralnetwork (RNN) for robust and accurate segmentation of the mandible. Such adesign of the system takes into account the similarity and continuity of themandible shapes captured in adjacent image slices in CT scans. The RSegCNNinfers the mandible information based on the recurrent structure with theembedded encoder-decoder segmentation (SegCNN) components. The recurrentstructure guides the system to exploit relevant and important information fromadjacent slices, while the SegCNN component focuses on the mandible shapes froma single CT slice. We conducted extensive experiments to evaluate the proposedRSegCNN on two head and neck CT datasets. The experimental results show thatthe RSegCNN is significantly better than the state-of-the-art models foraccurate mandible segmentation.

Recurrent convolutional neural networks for mandible segmentation from computed tomography