Fast Marching Energy CNN
Nicolas Makaroff Théo Bertrand Laurent D. Cohen
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.