The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional (3D) anatomy. Virtual reality (VR) surgical simulators have proven to be effective for surgical training. In this paper a fully automated method is proposed for segmenting multiple temporal-bone structures based on micro computed tomography (micro-CT) images for a realistic virtual environment. An automated segmentation pipeline is proposed based on a three-dimensional, fully convolutional neural network. The proposed balanced subsampling strategy creates balanced learning among the labels of multiple anatomical structures and reduces the class imbalance. The accuracy and speed of the proposed algorithm outperforms current manual and semi-automated segmentation techniques. The average Dice similarity scores for all temporal-bone structures was 88%. The proposed algorithm was validated on low-resolution CTs scanned by other centers with different scanner parameters than the ones used to create the algorithm. The presented fully automated segmentation algorithm creates 3D models of multiple structures of temporal-bone anatomy from micro- CT images with sufficient accuracy to be used in VR surgical training simulators.