Determining depth in human colliculi is prone to errors due to its rapidly varying curvature. Thus, Euclidean definitions of depth fall short in describing the topography of deep collicular tissue. We developed a method of surface-based depth mapping that calculates a depth coordinate using an algebraic level-set method. We then generated kernels based on this level-set depth that sample functional data in a nonlinear trajectory with increasing depth. Using this method, we re-analyzed data on polar-angle representation of saccadic eye movements and were able to produce smoother laminar profiles that confirmed distinct depth-dependence of saccade-evoked activity.