An Upgraded Siamese Neural Network for Motion Tracking in Ultrasound Image Sequences

Deep learning is heavily being borrowed to solve problems in medical imaging applications, and Siamese neural networks are the front runners of motion tracking. In this article, we propose to upgrade one such Siamese architecture-based neural network for robust and accurate landmark tracking in ultrasound images to improve the quality of image-guided radiation therapy. Although several researchers have improved the Siamese architecture-based networks with sophisticated detection modules and by incorporating transfer learning, the inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We employ a reference template update to resolve the constant position model and a linear Kalman filter (LKF) to address the missing motion model. Moreover, we demonstrate that the proposed architecture provides promising results without transfer learning. The proposed model was submitted to an open challenge organized by MICCAI and was evaluated exhaustively on the Liver US Tracking (CLUST) 2D dataset. Experimental results proved that the proposed model tracked the landmarks with promising accuracy. Furthermore, we also induced synthetic occlusions to perform a qualitative analysis of the proposed approach. The evaluations were performed on the training set of the CLUST 2D dataset. The proposed method outperformed the original Siamese architecture by a significant margin.