Abstract
Anterior cranial base (ACB) is known as the growth-stable structure. Automatic segmentation of the ACB is a prerequisite to superimpose orthodontic inter-treatment cone-beam computed tomography (CBCT) images. The automatic ACB segmentation is still a challenging task because of the ambiguous intensity distributions around fine-grained structures and artifacts due to the limited radiation dose. We propose a fully automatic segmentation of the ACB from CBCT images by a volumetric convolutional network with nested residual connections (NRN). The multi-scale feature fusion in the NRN not only promotes the information flows, but also introduces the supervision to multiple intermediate layers to speed up the convergence. The multi-level shortcut connections augment the feature maps in the decompression pathway and the end-to-end voxel-wise label prediction. The proposed NRN has been applied to the ACB segmentation from clinically-captured CBCT images. The quantitative assessment over the practitioner-annotated ground truths demonstrates the proposed method produces improvements to the state-of-the-arts.
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This work was supported by National Natural Science Foundation of China under Grant 61272342.
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Pei, Y. et al. (2017). Multi-scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_15
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DOI: https://doi.org/10.1007/978-3-319-67389-9_15
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