Surgical Retained Foreign Objects (RFOs) cause significant morbidity and mortality. They are associated with $1.5 billion annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. We address the RFO problem with two complementary technologies: a three dimensional (3D) Gossypiboma Micro Tag (μTa) that improves the visibility of RFOs on radiographs, and a Computer Aided Detection (CAD) system that detects the μTag. The 3D geometry of the μTag produces a similar 2D depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. We create a database of cadaveric radiographs with the μTag and other common man-made objects positioned randomly. We develop the CAD modules that include preprocessing, μTag enhancement, labeling, segmentation, feature analysis, classification and detection. The CAD can operate in a high specificity mode for the surgeon to allow for seamless workflow integration and function as a first reader. The CAD can also operate in a high sensitivity mode for the radiologist to ensure accurate detection. On a data set of 346 cadaveric radiographs, the CAD system performed at a high specificity (85.5% sensitivity, 0.02 FPs/image) for the OR and a high sensitivity (96% sensitivity, 0.73 FPs/image) for the radiologists.