Segmentation of knee cartilage from Ultrasound (US) images is essential for various clinical tasks in diagnosis and treatment planning of Osteoarthritis. Moreover, the potential use of US imaging for guidance in robotic knee arthroscopy is presently being investigated. The femoral cartilage being the main organ at risk during the operation, it is paramount to be able to segment this structure, to make US guidance feasible. In this paper, we set forth a deep learning network, Mask R-CNN, based femoral cartilage segmentation in 2D US images for these types of applications. While the traditional imaging approaches showed promising results, they are mostly not real-time and involve human interaction. This being the case, in recent years, deep learning has paved its way into medical imaging showing commendable results. However, deep learning-based segmentation in US images remains unexplored. In the present study we employ Mask R-CNN on US images of the knee cartilage. The performance of the method is analyzed in various scenarios, with and without Gaussian filter preprocessing and pretraining the network with different datasets. The best results are observed when the images are preprocessed and the network is pretrained with COCO 2016 image dataset. A maximum Dice Similarity Coefficient (DSC) of 0.88 and an average DSC of 0.80 is achieved when tested on 55 images indicating that the proposed method has a potential for clinical applications.