Recurrent convolutional neural networks for mandible segmentation from computed tomography

Understand

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.