  Automatic medical image segmentation is a crucial topic in the medical domain
and successively a critical counterpart in the computer-aided diagnosis
paradigm. U-Net is the most widespread image segmentation architecture due to
its flexibility, optimized modular design, and success in all medical image
modalities. Over the years, the U-Net model achieved tremendous attention from
academic and industrial researchers. Several extensions of this network have
been proposed to address the scale and complexity created by medical tasks.
Addressing the deficiency of the naive U-Net model is the foremost step for
vendors to utilize the proper U-Net variant model for their business. Having a
compendium of different variants in one place makes it easier for builders to
identify the relevant research. Also, for ML researchers it will help them
understand the challenges of the biological tasks that challenge the model. To
address this, we discuss the practical aspects of the U-Net model and suggest a
taxonomy to categorize each network variant. Moreover, to measure the
performance of these strategies in a clinical application, we propose fair
evaluations of some unique and famous designs on well-known datasets. We
provide a comprehensive implementation library with trained models for future
research. In addition, for ease of future studies, we created an online list of
U-Net papers with their possible official implementation. All information is
gathered in https://github.com/NITR098/Awesome-U-Net repository.
