Murine Organ Auto-Contouring in Small-Animal Precision Irradiation: A Comprehensive Approach Integrating Deep Learning and Contrast Enhancement for Onboard CBCT

Modern preclinical irradiators have evolved to mimic their clinical Linac counterparts in terms of 360-degree beam delivery and on-board imaging capabilities with CBCT. The primary factor preventing widespread 3D conformal small-animal RT is the necessity of manually segmentation, as this task is time-consuming and impractical for large-scale studies. Although DL-based auto-contouring methods have been explored for preclinical irradiator CBCT, these methods have been limited to high-contrast, minimally anatomically complex structures. Thus, DL-based segmentation for low-contrast abdominal structures has yet to be addressed. In combining DL with iodine-based contrast-agent, precise full-body auto-contouring was achieved. A U-net-like architecture was trained to contour kidneys, spinal cord, stomach, liver, bowels, heart, lungs, and bones in small-animal irradiator CBCT mouse scans. Post contrast-enhancement, 41 mice were manually contoured, establishing ground truths. The model was trained with 26 mice, 2 for validation, and 15 for testing. Performance was evaluated using dice, precision, HD, and MSD. The proposed model predicted high-quality contours within a second, with the median for all organs reported: dice > 97%, precision > 98%, HD ₉₅  < 2.15 mm, and MSD < 0.55 mm. The proposed combination of a DL and contrast-enhanced model is a viable method to vastly improve efficiency of small-animal IGRT.