  Automatic medical image segmentation, an essential component of medical image
analysis, plays an importantrole in computer-aided diagnosis. For example,
locating and segmenting the liver can be very helpful in livercancer diagnosis
and treatment. The state-of-the-art models in medical image segmentation are
variants ofthe encoder-decoder architecture such as fully convolutional network
(FCN) and U-Net.1A major focus ofthe FCN based segmentation methods has been on
network structure engineering by incorporating the latestCNN structures such as
ResNet2and DenseNet.3In addition to exploring new network structures for
efficientlyabstracting high level features, incorporating structures for
multi-scale image feature extraction in FCN hashelped to improve performance in
segmentation tasks. In this paper, we design a new multi-scale
networkarchitecture, which takes multi-scale inputs with dedicated
convolutional paths to efficiently combine featuresfrom different scales to
better utilize the hierarchical information.
