  U-Net is a widely adopted neural network in the domain of medical image
segmentation. Despite its quick embracement by the medical imaging community,
its performance suffers on complicated datasets. The problem can be ascribed to
its simple feature extracting blocks: encoder/decoder, and the semantic gap
between encoder and decoder. Variants of U-Net (such as R2U-Net) have been
proposed to address the problem of simple feature extracting blocks by making
the network deeper, but it does not deal with the semantic gap problem. On the
other hand, another variant UNET++ deals with the semantic gap problem by
introducing dense skip connections but has simple feature extraction blocks. To
overcome these issues, we propose a new U-Net based medical image segmentation
architecture R2U++. In the proposed architecture, the adapted changes from
vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper
recurrent residual convolution block. The increased field of view with these
blocks aids in extracting crucial features for segmentation which is proven by
improvement in the overall performance of the network. (2) The semantic gap
between encoder and decoder is reduced by dense skip pathways. These pathways
accumulate features coming from multiple scales and apply concatenation
accordingly. The modified architecture has embedded multi-depth models, and an
ensemble of outputs taken from varying depths improves the performance on
foreground objects appearing at various scales in the images. The performance
of R2U++ is evaluated on four distinct medical imaging modalities: electron
microscopy (EM), X-rays, fundus, and computed tomography (CT). The average gain
achieved in IoU score is 1.5+-0.37% and in dice score is 0.9+-0.33% over
UNET++, whereas, 4.21+-2.72 in IoU and 3.47+-1.89 in dice score over R2U-Net
across different medical imaging segmentation datasets.
