To
perform this investigation, 2 datasets were used: 160 prostate NECTs, and a
further 6 clinical excretory-phase prostate CECTs. CTs had corresponding
structure sets of organs at risk (OARs), delineated by experienced dosimetrists.
A
generative appearance model with 8 degrees of freedom (DOFs) was inferred from real CECTs
and used to simulate CECT images from NECT data. An example NECT and a
corresponding generated CECT are shown in Figure 1.
A
synthetic CECT was then generated for each NECT, forming an augmented dataset
of 320 cases. Values for the DOFs were randomly sampled from the
learned distributions.
Two separate CNN-based OAR DLC network models were trained, using the same architecture:
- DLC: Trained
using NECT data only
- AugDLC: Trained using both NECTs and synthetic CECTs
80%
of each dataset was randomly assigned to network training, 10% to
validation, and 10% to testing.
Both
networks were applied to the 6 clinical CECTs to produce contours. They were
also applied to 12 NECTs not used in training. Structures for 4 OARs were autocontoured:
the bladder, prostate, rectum, and seminal vesicles. These were compared to the
expert delineations using 3D Dice similarity coefficient (DSC) and added path
length (APL). APL is correlated to the time required to edit a contour to a clinically-acceptable
standard.
