A U-Shaped Convolution Recurrent Network for Spatiotemporal Feature Fusion in 4D-CT Image Registration
Zesen Yu, Yu Ji, Jiaji Liu, Ying Wei · 2023
In lung 4DCT registration, pairwise methods do not fully utilize the temporal component of serial images. Recently some studies have tried to address this issue. However, interpretability and high-dimensional feature modeling in a temporally explicit manner still need to be further explored. In this paper, a U-shaped network based on Conv-LSTM was proposed that defines image registration as a spatiotemporal sequence prediction problem. The backbone structure consists of stacked Conv-LSTM layers, and a time channel is introduced to fuse the spatio-temporal features of image sequences. We train model in one-shot style on DIRLAB dataset. The experimental results demonstrate that the proposed method achieves better registration performance in spatiotemporal sequence image registration tasks compared with current works.