Abstract
Masticatory muscles are of significant aesthetic and functional importance to craniofacial developments. Automatic segmentation is a crucial step for shape and functional analysis of muscles. In this paper, we propose an automatic masseter segmentation framework using a deep neural network with coupled feature learning and label prediction pathways. The volumetric features are learned using the unsupervised convolutional auto-encoder and integrated with multi-level features in the label prediction pathway to augment features for segmentation. The label prediction pathway is built upon the nested residual network which is feasible for information propagation and fast convergence. The proposed method realizes the voxel-wise label inference of masseter muscles from the clinically captured computed tomography (CT) images. In the experiments, the proposed method outperforms the compared state-of-the-arts, achieving a mean Dice similarity coefficient (DSC) of \(93\pm 1.2\%\) for the segmentation of masseter muscles.
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This work was supported by NSFC 61272342.
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Qin, H., Pei, Y., Guo, Y., Ma, G., Xu, T., Zha, H. (2018). Masseter Segmentation from Computed Tomography Using Feature-Enhanced Nested Residual Neural Network. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_41
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