Manual segmentation of articular cartilage and menisci from magnetic resonance data is time-consuming and can be challenging. Recently, deep learning has shown promising results in medical image segmentation. The aim of this study was to develop a method for automatic segmentation of articular cartilage and menisci that performs more accurately and efficiently than the previously published methods. On OAI/iMorphics dataset the method achieves Dice score of 90.7±1.9 for femoral cartilage, 89.7±2.8 for tibial, 87.1±4.7 for patellar, and 86.3±3.4 for menisci. The presented results could facilitate the osteoarthritis research and enhance clinical practice. Source codes and pretrained models will be open-sourced.