  Purpose. To alleviate the manual contouring burden, deep learning (DL) based
automated contouring has been explored. However, due to the poor contrast
resolution of preclinical irradiator CBCT, these methods have been limited to
high contrast - minimally anatomically complex - structures such as the heart
and lungs. Thus, low contrast abdominal CBCT DL-based segmentation has yet to
be addressed. In this work we explore a DL-based model in conjunction with
iodine-based contrast agent approach to allow precise automatic contouring of
mouse abdominal, thorax, and skeletal structures in under a second.
  Methods. A DL U-net-like architecture was trained to contour mice organs in
small animal radiation research platform CBCT scans. 41 mice were contoured by
a human expert, using semi-automatic segmentation methods, after injection of
iodine contrast agent, establishing a ground truth for the DL model. The model
was trained on a dataset of 26 mice, while 2 mice were used for validation,
tuning the model during training, and 15 mice used for performance evaluation
testing. The model consists of a pre-processor, and a post-processor for
volumetric reconstruction of the DL-predicted probability maps. Model
performance was evaluated using both qualitative and distance metrics,
including the dice similarity score, precision score, Hausdorff Distance (HD),
and mean surface distance (MSD).
  Results. Performance of the DL-based iodine contrast-enhanced model provided
high quality predicted contours in under a second, with the median for all
organs being reported: dice $>$ 91\%, precision $>$ 95\%, HD50 $<$ 1.0 mm, and
MSD $<$ 1.41 mm.
  Conclusion. The proposed combination of a DL-based and iodine
contrast-enhanced model proved as a viable method to vastly improve efficiency
of small animal CBCT image-guided RT preclinical trials.
