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Abstract
In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation.
In this regard, U-Net has been the most popular architecture in the medical imaging
community. Despite outstanding overall performance in segmenting multimodal medical
images, through extensive experimentations on some challenging datasets, we demonstrate
that the classical U-Net architecture seems to be lacking in certain aspects. Therefore,
we propose some modifications to improve upon the already state-of-the-art U-Net model.
Following these modifications, we develop a novel architecture, MultiResUNet, as the
potential successor to the U-Net architecture. We have tested and compared MultiResUNet
with the classical U-Net on a vast repertoire of multimodal medical images. Although
only slight improvements in the cases of ideal images are noticed, remarkable gains
in performance have been attained for the challenging ones. We have evaluated our
model on five different datasets, each with their own unique challenges, and have
obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%, 1.41%, and
0.62% respectively. We have also discussed and highlighted some qualitatively superior
aspects of MultiResUNet over classical U-Net that are not really reflected in the
quantitative measures.