A fully-automated segmentation pipeline was built by combining a deep Convolutional Auto-Encoder (CAE) network and 3D simplex deformable modeling. The CAE was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined output from CAE to preserve the overall shape and maintain a desirable smooth surface for structure. The fully-automated segmentation method was tested using a publicly available knee joint image dataset to compare with currently used state-of-the-art segmentation methods. The fully-automated method was also evaluated on morphological MR images with different tissue contrasts and image training datasets.