Segmentation of image sequences is an important task in medical image
analysis, which enables clinicians to assess the anatomy and function of moving
organs. However, direct application of a segmentation algorithm to each time
frame of a sequence may ignore the temporal continuity inherent in the
sequence. In this work, we propose an image sequence segmentation algorithm by
combining a fully convolutional network with a recurrent neural network, which
incorporates both spatial and temporal information into the segmentation task.
A key challenge in training this network is that the available manual
annotations are temporally sparse, which forbids end-to-end training. We
address this challenge by performing non-rigid label propagation on the
annotations and introducing an exponentially weighted loss function for
training. Experiments on aortic MR image sequences demonstrate that the
proposed method significantly improves both accuracy and temporal smoothness of
segmentation, compared to a baseline method that utilises spatial information
only. It achieves an average Dice metric of 0.960 for the ascending aorta and
0.953 for the descending aorta.

本文提出了一种将全卷积网络和循环神经网络相结合的图像序列分割算法，旨在将空间和时间信息同时纳入分割任务中，通过非刚性标签传播和指数加权损失函数的引入来解决训练中存在的挑战，实现了对人体主动脉 MRI 图像序列的高精度分割。