  With the advent of advancements in deep learning approaches, such as deep
convolution neural network, residual neural network, adversarial network; U-Net
architectures are most widely utilized in biomedical image segmentation to
address the automation in identification and detection of the target regions or
sub-regions. In recent studies, U-Net based approaches have illustrated
state-of-the-art performance in different applications for the development of
computer-aided diagnosis systems for early diagnosis and treatment of diseases
such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various
modalities. This article contributes in presenting the success of these
approaches by describing the U-Net framework, followed by the comprehensive
analysis of the U-Net variants by performing 1) inter-modality, and 2)
intra-modality categorization to establish better insights into the associated
challenges and solutions. Besides, this article also highlights the
contribution of U-Net based frameworks in the ongoing pandemic, severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.
Finally, the strengths and similarities of these U-Net variants are analysed
along with the challenges involved in biomedical image segmentation to uncover
promising future research directions in this area.
