  The encoder-decoder networks are commonly used in medical image segmentation
due to their remarkable performance in hierarchical feature fusion. However,
the expanding path for feature decoding and spatial recovery does not consider
the long-term dependency when fusing feature maps from different layers, and
the universal encoder-decoder network does not make full use of the
multi-modality information to improve the network robustness especially for
segmenting medical MRI. In this paper, we propose a novel feature fusion unit
called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to
memorize the long-term context information from the previous layers in the
decoding phase. An encoder-decoder network, named Convolutional Recurrent
Decoding Network (CRDN), is also proposed based on RDC for segmenting
multi-modality medical MRI. CRDN adopts CNN backbone to encode image features
and decode them hierarchically through a chain of RDCs to obtain the final
high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and
HVSMR datasets demonstrate that the introduction of RDC effectively improves
the segmentation accuracy as well as reduces the model size, and the proposed
CRDN owns its robustness to image noise and intensity non-uniformity in medical
MRI.
