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Image segmentation is a branch of computer vision that is widely used in realworld applications including biomedical image processing. With recentadvancement of deep learning, image segmentation has achieved at a very highlevel performance. Recently, UNet architecture is found as the core of noveldeep learning segmentation methods. In this paper we implement UNetarchitecture from scratch with using basic blocks in Pytorch and evaluate itsperformance on multiple biomedical image datasets. We also use transferlearning to apply novel modified UNet segmentation packages on the biomedicalimage datasets. We fine tune the pre-trained transferred model with eachspecific dataset. We compare its performance with our fundamental UNetimplementation. We show that transferred learning model has better performancein image segmentation than UNet model that is implemented from scratch.

Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning