Semi-Supervised 3D Medical Image Segmentation Via Boundary-Aware Consistent Hidden Representation Learning

Partitioning of images is a fundamental image processing task, which is closely related to various problems and applications in computer vision. Due to the hard nature of the underlying problem, existing algorithms are very compute intense. in this work we present a stochastic algorithm to efficiently approximate solutions of the image partitioning problem. Our contributions lie in the novel convex reformulation of the underlying graph cut problem, along with the application of a stochastic solver. These changes allow a faster convergence compared to other graph cut based methods, which is confirmed by our experiments.