Full-body deep learning-based automated contouring of contrast-enhanced murine organs for small animal irradiator CBCT

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.