  Recently, deep learning (DL) found its way to interventional X-ray skin dose
estimation. While its performance was found to be acceptable, even more
accurate results could be achieved if more data sets were available for
training. One possibility is to turn to computed tomography (CT) data sets.
Typically, computed tomography (CT) scans can be mapped to tissue labels and
mass densities to obtain training data. However, care has to be taken to make
sure that the different clinical settings are properly accounted for. First,
the interventional environment is characterized by wide variety of table setups
that are significantly different from the typical patient tables used in
conventional CT. This cannot be ignored, since tables play a crucial role in
sound skin dose estimation in an interventional setup, e. g., when the X-ray
source is directly underneath a patient (posterior-anterior view). Second, due
to interpolation errors, most CT scans do not facilitate a clean segmentation
of the skin border. As a solution to these problems, we applied connected
component labeling (CCL) and Canny edge detection to (a) robustly separate the
patient from the table and (b) to identify the outermost skin layer. Our
results show that these extensions enable fully-automatic, generalized
pre-processing of CT scans for further simulation of both skin dose and
corresponding X-ray projections.
