  In this work, we propose a new segmentation network by integrating DenseUNet
and bidirectional LSTM together with attention mechanism, termed as
DA-BDense-UNet. DenseUNet allows learning enough diverse features and enhancing
the representative power of networks by regulating the information flow.
Bidirectional LSTM is responsible to explore the relationships between the
encoded features and the up-sampled features in the encoding and decoding
paths. Meanwhile, we introduce attention gates (AG) into DenseUNet to diminish
responses of unrelated background regions and magnify responses of salient
regions progressively. Besides, the attention in bidirectional LSTM takes into
account the contribution differences of the encoded features and the up-sampled
features in segmentation improvement, which can in turn adjust proper weights
for these two kinds of features. We conduct experiments on liver CT image data
sets collected from multiple hospitals by comparing them with state-of-the-art
segmentation models. Experimental results indicate that our proposed method
DA-BDense-UNet has achieved comparative performance in terms of dice
coefficient, which demonstrates its effectiveness.
