  Intracranial carotid artery calcification (ICAC) is a major risk factor for
stroke, and might contribute to dementia and cognitive decline. Reliance on
time-consuming manual annotation of ICAC hampers much demanded further research
into the relationship between ICAC and neurological diseases. Automation of
ICAC segmentation is therefore highly desirable, but difficult due to the
proximity of the lesions to bony structures with a similar attenuation
coefficient. In this paper, we propose a method for automatic segmentation of
ICAC; the first to our knowledge. Our method is based on a 3D fully
convolutional neural network that we extend with two regularization techniques.
Firstly, we use deep supervision (hidden layers supervision) to encourage
discriminative features in the hidden layers. Secondly, we augment the network
with skip connections, as in the recently developed ResNet, and dropout layers,
inserted in a way that skip connections circumvent them. We investigate the
effect of skip connections and dropout. In addition, we propose a simple
problem-specific modification of the network objective function that restricts
the focus to the most important image regions and simplifies the optimization.
We train and validate our model using 882 CT scans and test on 1,000. Our
regularization techniques and objective improve the average Dice score by 7.1%,
yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC
volumes and manual annotations.
