An efficient deep equilibrium model for medical image segmentation.

In this paper, we propose an effective method that takes the advantages of classical methods and deep learning technology for medical image segmentation through modeling the neural network as a fixed point iteration seeking for system equilibrium by adding a feedback loop. In particular, the nuclear segmentation of medical image is used as an example to demonstrate the proposed method where it can successfully complete the challenge of segmenting nuclei from cells in different histopathological images. Specifically, the nuclei segmentation is formulated as a dynamic process to search for the system equilibrium. Starting from an initial segmentation generated either by a classic algorithm or pre-trained deep learning model, a sequence of segmentation output is created and combined with the original image to dynamically drive the segmentation towards the expected value. This dynamical extension to neural networks requires little extra change on the backbone deep neural network while it significantly increased model accuracy, generalizability, and stability as demonstrated by intensive experimental results from pathological images of different tissue types across different open datasets.