Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images

•The femoral condyle cartilage is one of the structure most at risk during knee arthroscopy.•The first methodology to track in real-time the femoral condyle cartilage in ultrasound images.•Effective combination of a neural network architecture for medical image segmentation and the siamese framework for visual tracking.•Tracking performance comparable to two experienced surgeons.•Outperforming state-of-the-art segmentation models and trackers in the tracking of the femoral cartilage. [Display omitted] The tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurately and efficiently, the femoral condyle cartilage in ultrasound sequences, which were acquired under several clinical conditions, mimicking realistic surgical setups. Our solution, that we name Siam-U-Net, requires minimal user initialization and combines a deep learning segmentation method with a siamese framework for tracking the cartilage in temporal and spatio-temporal sequences of 2D ultrasound images. Through extensive performance validation given by the Dice Similarity Coefficient, we demonstrate that our algorithm is able to track the femoral condyle cartilage with an accuracy which is comparable to experienced surgeons. It is additionally shown that the proposed method outperforms state-of-the-art segmentation models and trackers in the localization of the cartilage. We claim that the proposed solution has the potential for ultrasound guidance in minimally invasive knee procedures.