Prostate cancer is the second leading cause of death due to cancer for men in the United States. A common form of treatment for prostate cancer is a surgical procedure known as a radical prostatectomy, the full resection of the prostate from the patient. The success of this procedure is quantified by the presence of diseased tissue at the surface of the resected specimen. The ideal outcome of this procedure is known as a negative surgical margin (NSM), indicating that there is no diseased tissue along the margin of the specimen, however positive surgical margins (PSM) occur regularly. Currently, the method for intra-operative detection is Frozen Section Analysis (FSA) but it suffers from a high false negative rate due to a small sampling size. Our group is developing a new method of ex vivo imaging of the entire radical prostatectomy surgical margins using video-rate structured illumination microscopy (VR-SIM) paired with a custom-built sample position system. The goal of this method is to rapidly image the entire circumference of the resected specimen in order to correct surgical margins intraoperatively. Currently, the custom-built apparatus used for manipulating the specimen, known as the Automated Prostate Positioning System (APPS), faces several challenges in order for the machine to be considered adoptable in a point-of-care environment. The goal of this work is to further refine the APPS with the intent of implementing this system for point-of-care applications. This will be achieved through three specific aims: (1) identify the existing challenges faced in clinical applications of the current APPS configuration, (2) use the challenges identified with the current configuration of the APPS to implement structural and software changes to better suit clinical applications, and (3) verify that the new APPS configuration is an improvement of the prior iteration through bench experiments.