Active Contour Model in Deep Learning Era: A Revise and Review

Active Contour (AC)-based segmentation has been widely used to solve many image processing problems, specially image segmentation. While these AC-based methods offer object shape constraints, they typically look for strong edges or statistical modeling for successful segmentation. Clearly, AC-based approaches lack a way to work with labeled images in a supervised machine learning framework. Furthermore, they are unsupervised approaches and strongly depend on many parameters which are chosen by empirical results. Recently, Deep Learning (DL) has become the go-to method for solving many problems in various areas. Over the past decade, DL has achieved remarkable success in various artificial intelligence research areas. DL is supervised methods and requires large volume ground-truth. This paper first provides a fundamental of both Active Contour techniques and Deep Learning framework. We then present the state-of-the-art approaches of Active Contour techniques incorporating in Deep Learning framework.