We present an active surface-based method for refining the boundary surfaces of mitochondria segmentation data. We exploit the fact that mitochondria have thick dark membranes, so referencing the image data at the inner membrane can help drive a more accurate delineation of the outer membrane surface. Given the initial boundary prediction from a machine learning-based segmentation algorithm as input, we compare several cost functions used to drive an explicit update scheme to locally refine 3D mesh surfaces, and results are presented on electron microscopy imagery. Our resulting surfaces are seen to fit very accurately to the mitochondria membranes, more accurately even than the available hand-annotations of the data.