Fully Automated 3D Colon Segmentation and Volume Rendering in Virtual Reality

Deep learning algorithms have provided efficient and effective approaches for biomedical image segmentation. However, it is difficult for an untrained user to mentally or visually restore the 3D geometry of the segmentation result from MRI image stacks. Existing MRI image reconstruction approaches require human intervention, and the learning curve makes clinical adoption even harder. By incorporating U-Net and volumetric rendering techniques, we present an automatic and intuitive application to assist surgeons to visualize and identify particular areas in MRI scan. Our system can accurately segment the colon section and create a manipulable 3D volume rendered in virtual reality environment with mask alpha blended. We hope that our system can support doctors to conduct biomedical imaging analysis and surgical planning in real clinical scenarios.