  Computed Tomography (CT) of the temporal bone has become an important method
for diagnosing ear diseases. Due to the different posture of the subject and
the settings of CT scanners, the CT image of the human temporal bone should be
geometrically calibrated to ensure the symmetry of the bilateral anatomical
structure. Manual calibration is a time-consuming task for radiologists and an
important pre-processing step for further computer-aided CT analysis. We
propose an automatic calibration algorithm for temporal bone CT images. The
lateral semicircular canals (LSCs) are segmented as anchors at first. Then, we
define a standard 3D coordinate system. The key step is the LSC segmentation.
We design a novel 3D LSC segmentation encoder-decoder network, which introduces
a 3D dilated convolution and a multi-pooling scheme for feature fusion in the
encoding stage. The experimental results show that our LSC segmentation network
achieved a higher segmentation accuracy. Our proposed method can help to
perform calibration of temporal bone CT images efficiently.
