Fast Marching Energy CNN
This work addresses the challenge of enforcing geometric and topological constraints in medical imaging segmentation, which is crucial for accurate diagnosis and treatment planning, though it appears incremental as it builds on existing geodesic distance methods.
The paper tackled the problem of incorporating geometric constraints into image segmentation by introducing a method that uses CNNs to generate isotropic Riemannian metrics for computing geodesic distances, applied to brain tumor segmentation to achieve state-of-the-art performance while ensuring geometric and topological properties.
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.