  Image segmentation is a branch of computer vision that is widely used in real
world applications including biomedical image processing. With recent
advancement of deep learning, image segmentation has achieved at a very high
level performance. Recently, UNet architecture is found as the core of novel
deep learning segmentation methods. In this paper we implement UNet
architecture from scratch with using basic blocks in Pytorch and evaluate its
performance on multiple biomedical image datasets. We also use transfer
learning to apply novel modified UNet segmentation packages on the biomedical
image datasets. We fine tune the pre-trained transferred model with each
specific dataset. We compare its performance with our fundamental UNet
implementation. We show that transferred learning model has better performance
in image segmentation than UNet model that is implemented from scratch.
