Automatic segmentation of the knee menisci would facilitate quantitative and morphological evaluation in diseases such as osteoarthritis. We propose a deep convolutional neural network for the segmentation of 3D UTE-Cones Adiabatic T1ρ-weighted volumes of the meniscus. To show the usefulness of the proposed method, we developed the models using regions of interests provided by two radiologists. The method produced strong Dice scores and consistent results with respect to meniscus volume measurement. The inter-observer agreement between the models and the radiologists was very similar to that of the radiologists alone.