  The paper is a short review of medical image segmentation using U-Net and its
variants. As we understand going through a medical images is not an easy job
for any clinician either radiologist or pathologist. Analysing medical images
is the only way to perform non-invasive diagnosis. Segmenting out the regions
of interest has significant importance in medical images and is key for
diagnosis. This paper also gives a bird eye view of how medical image
segmentation has evolved. Also discusses challenge's and success of the deep
neural architectures. Following how different hybrid architectures have built
upon strong techniques from visual recognition tasks. In the end we will see
current challenges and future directions for medical image segmentation(MIS).
