  Automatic lung lesions segmentation of chest CT scans is considered a pivotal
stage towards accurate diagnosis and severity measurement of COVID-19.
Traditional U-shaped encoder-decoder architecture and its variants suffer from
diminutions of contextual information in pooling/upsampling operations with
increased semantic gaps among encoded and decoded feature maps as well as
instigate vanishing gradient problems for its sequential gradient propagation
that result in sub-optimal performance. Moreover, operating with 3D CT-volume
poses further limitations due to the exponential increase of computational
complexity making the optimization difficult. In this paper, an automated
COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient
neural network architecture, namely CovSegNet, to overcome these limitations.
Additionally, a two-phase training scheme is introduced where a deeper
2D-network is employed for generating ROI-enhanced CT-volume followed by a
shallower 3D-network for further enhancement with more contextual information
without increasing computational burden. Along with the traditional vertical
expansion of Unet, we have introduced horizontal expansion with multi-stage
encoder-decoder modules for achieving optimum performance. Additionally,
multi-scale feature maps are integrated into the scale transition process to
overcome the loss of contextual information. Moreover, a multi-scale fusion
module is introduced with a pyramid fusion scheme to reduce the semantic gaps
between subsequent encoder/decoder modules while facilitating the parallel
optimization for efficient gradient propagation. Outstanding performances have
been achieved in three publicly available datasets that largely outperform
other state-of-the-art approaches. The proposed scheme can be easily extended
for achieving optimum segmentation performances in a wide variety of
applications.
