Although many three-dimensional (3D) medical imaging visualization methods exist, 3D volume slicing remains the most commonly used technique for visualizing medical data from modalities such as CT, MRI, and PET. We propose to extend the possibilities of oblique slicing to developable curved surfaces that can be flattened and displayed in two dimensions without deformation. Such surfaces can be used to follow curved anatomical structures while preserving distance metrics at visualization time. They may also be useful for the staging of tumors, i.e., to evaluate the spatial extension of a tumor. We propose an out of core algorithm that runs in parallel on a multi-PC architecture and is able to extract surfaces from very large 3D datasets such as the visible human data set (man: 13 GB, woman: 49 GB). Experimental performance results are presented which demonstrate that parallel surface extraction is scalable and has a reasonable overhead compared with traditional oblique planar slicing. Surface extraction is made available to the public as one of the services offered by EPFLs visible human web server (