Ex-vivo Porcine Model for Generating an Internal Surface in Biological Subjects using 3D-GAN

Abstract

Robotic automation in the medical industry is complex because of the biological nature of the processed materials and the risks to the patient. Everyone is different, which means that each individual has their own characteristics; different size, bone structure, joint positions, etc. This makes it hard for robots to autonomously operate on humans. RGB-D consumer devices and computing power are revolutionising the way robots interact with the environment. With the camera’s intrinsic parameters and the depth frame, a point cloud, i.e., a set of points in a Cartesian space IR3, can be generated giving more complete information to the application in real-time. This work investigates a GAN (Generative Adversarial Network) to generate the internal surface of an ex-vivo porcine left ham, as a precursor to consideration of human models. That is important as a prediction of the internal structure. A good internal ham surface could be generated even with a small dataset. The complexity of the shapes in the generated data are shown and structures like the ball-joint attachment can be seen.