A retrospective study of 21 patients with localized prostate cancer evaluated CT-based automatic segmentation of a non-radiopaque hydrogel rectal spacer (SpaceOAR; Boston Scientific) using a commercial deep learning platform (OncoStudio version 2.0; Oncosoft Inc.). Reference contours were manually delineated on planning CT with reference to co-registered T2-weighted MRI. Geometric accuracy for the rectal spacer showed a mean Dice similarity coefficient (DSC) of 0.787 ± 0.073, a mean 95th-percentile Hausdorff distance (HD₉₅) of 4.03 ± 1.83 mm, and a mean mean surface distance (MSD) of 1.18 ± 0.63 mm. Adjacent organs showed high geometric agreement, with mean DSC values of 0.876, 0.890, and 0.963 for the prostate, rectum, and bladder, respectively. No systematic over- or under-segmentation was observed, and the MSD for all structures was below 2.5 mm. The study concluded that CT-based automatic segmentation using OncoStudio provided clinically acceptable boundary delineation of a non-radiopaque hydrogel rectal spacer without requiring MRI, while maintaining contouring quality of surrounding organs, suggesting that CT-only automatic contouring can reduce spacer-specific MRI use and associated registration uncertainty in prostate radiotherapy.