Symmetric convolutional neural network for mandible segmentation

Abstract

Mandible segmentation is an essential step in craniomaxillofacial surgery planning, which aims to segment mandible from multi-slice computed tomography (MSCT) images. One main disadvantage of most existing mandible segmentation methods is that they require numerous expert knowledge for semi-automatic segmentation. However, the high-quality expert knowledge is hard to achieve in practice due to the scarcity of experienced doctors and experts. To solve this problem, we propose an end-to-end trainable deep learning based method, which performs segmentation in an automatic, accurate and efficient manner. Different from the popular convolutional neural network (CNN), our proposed symmetric convolutional neural network (SCNN) enforces convolution and deconvolution computation to be symmetric as to achieve a good segmentation performance. Furthermore, benefiting from the manner of end-to-end, SCNN could automatically perform mandible segmentation from raw image data. Such advantages remarkably reduce the human effort and achieve competitive performance. To verify the effectiveness of our method, we build a multi-slice computer tomography mandible dataset which includes 93 cases. The experimental results show that the proposed SCNN is superior to several popular baselines in terms of the dice similarity coefficient (DSC).