A random-forest random field approach for cellular image segmentation

The formulation of energy minimization of Markov random fields has been extensively utilized to infer pixel labels in cellular image segmentation, where a crucial step is to specify the data and discontinuity penalty terms in energy functions. In this paper, we propose a random forest based approach to directly learn the respective energy terms from the data. Empirical experiments indicate that our approach outperforms state-of-the-art methods.