Image segmentation (IS) is the most challenging and fundamental task in computer vision for analyzing medical images. Amongst several medical image segmentation methods available in the literature, Persistent Homology (PH) based methods are relatively new and have several good features with many applications in medical imaging. PH is a rapidly growing technique from Topological Data Analysis (TDA). TDA is a decade-old field built on the tools from Algebraic Topology (branch of pure mathematics), which studies the shape of data. TDA is rapidly growing due to its unique way of analyzing inferring exploiting complex data sets. In PH-based methods, images are converted to point clouds; then, simplicial complexes are constructed at different varying scales on the top of point clouds using the filtration process. Several topological features like connected components loops or holes are obtained during the filtration process at different scales. Then Image is segmented based on these features. This review is devoted to PH-based medical segmentation. All the methods are systematically reviewed. In addition, PH with a deep learning segmentation technique is also discussed.