Fast Marching Energy CNN

TL;DR

This paper introduces a novel CNN-based method for computing geodesic distances using isotropic Riemannian metrics, improving brain tumor segmentation by incorporating geometrical and topological constraints.

Contribution

The paper presents a new approach combining CNNs with Riemannian metrics to enhance geodesic distance computation for image segmentation tasks.

Findings

Achieves state-of-the-art brain tumor segmentation performance.

Effectively incorporates geometrical and topological constraints.

Demonstrates compatibility of geodesic modules with machine learning frameworks.

Abstract

Leveraging geodesic distances and the geometrical information they convey is key for many data-oriented applications in imaging. Geodesic distance computation has been used for long for image segmentation using Image based metrics. We introduce a new method by generating isotropic Riemannian metrics adapted to a problem using CNN and give as illustrations an example of application. We then apply this idea to the segmentation of brain tumours as unit balls for the geodesic distance computed with the metric potential output by a CNN, thus imposing geometrical and topological constraints on the output mask. We show that geodesic distance modules work well in machine learning frameworks and can be used to achieve state-of-the-art performances while ensuring geometrical and/or topological properties.