Neural network parametrized level sets for image segmentation

TL;DR

This paper introduces a neural network-based parametrization of level set functions for image segmentation, extending Chan-Vese algorithms with data-driven initialization for improved efficiency and convergence.

Contribution

It proposes a novel neural network parametrization of level set functions, enabling data-driven Chan-Vese segmentation with learned parameters for better performance.

Findings

Neural networks can efficiently approximate polygonal level set functions.

The approach improves initialization and convergence speed of Chan-Vese segmentation.

Unsupervised training encodes geometric data structures into the network parameters.

Abstract

Chan-Vese algorithms have proven to be a first-class method for image segmentation. Early implementations used level set methods with a pixelwise representation of the level set function. Later, parametrized level set approximations, such as splines, have been studied and computationally developed to improve efficiency. In this paper, we use neural networks as parametrized approximations of level set functions for implementing the Chan-Vese methods. We show that this approach is efficient because of the equivalence between two layer neural networks and polygonal approximations of level set-based segmentations. In turn, this allows the two-layer network architecture to be interpreted as an ansatz function for the approximate minimization of Chan-Vese functionals. Based on these theory, we extend the classical Chan-Vese algorithm to a data-driven setting, where prior parameters of the network are obtained through unsupervised training on representative image data. These learned parameters encode geometric structures of the data, leading to improved initialization and faster convergence of the Chan-Vese image segmentation.