Deep neural networks for fast segmentation of 3D medical images

During the last years Deep Learning and especially Convolutional Neural Networks (CNN) have set new standards for different computer vision tasks like image classification and semantic segmentation. In this paper,a CNN for 3D volume segmentation based on recently introduced deep learning components will be presented. In addition to using image patches as input for a CNN,the usage of orthogonal patches,which combine shape and locality information with intensity information for CNN training will be evaluated. For this purpose a publically available CT dataset of the head-neck region has been used and the results have been compared with other state-of-the art atlas- and model-based segmentation approaches. The presented approach is fully automated,fast and not restricted to specific anatomical structures. Quantitative evaluation provides good results and shows the great potential of deep learning approaches for the segmentation of medical images.