Computer Graphics and Visual Computing (CGVC) 2018
pp. 109 - 113
Segmenting Teeth from Volumetric CT Data with a Hierarchical CNN-based Approach
Author(s):
Philipp Marten Macho, Nadja Kurz, Adrian Ulges, Robert Brylka, Thomas
Gietzen, and Ulrich Schwanecke
DOI:
10.2312/cgvc.20181213
Abstract:
This paper addresses the automatic segmentation of teeth in volumetric
Computed Tomography (CT) scans of the human skull. Our approach is based
on a convolutional neural network employing 3D volumetric convolutions.
To tackle data scale issues, we apply a hierarchical coarse-to fine
approach combining two CNNs, one for low-resolution detection and one for
highresolution refinement. In quantitative experiments on 40 CT scans
with manually acquired ground truth, we demonstrate that our approach
displays remarkable robustness across different patients and device
vendors. Furthermore, our hierarchical extension outperforms a
single-scale segmentation, and network size can be reduced compared to
previous architectures without loss of accuracy.
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CCS Concepts: Computer Graphics --> Image processing; Computing / Technology Policy --> Medical technologies; Machine Learning --> Neural networks
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