Weekly supervised convolutional long short-term memory neural networks for MR-TRUS registration

Abstract

We propose an approach based on a weekly supervised method for MR-TRUS image registration. Inspired by the viscous fluid physical model, we made the first attempt at combining convolutional neural network (CNN) and long short-term memory (LSTM) Neural Network to perform deep learning-based dense deformation field prediction. Through the integration of convolutional long short-term memory (ConvLSTM) Neural Network and weakly supervised approach, we achieved accurate results in terms of Dice similarity coefficient (DSC) and target registration error (TRE) without using conventional intensity-based image similarity measures. Thirty-six sets of patient data were used in the study. Experimental results showed that our proposed ConvLSTM neural network produced a mean TRE of 2.85±1.72 mm and a mean Dice of 0.89.