Neuron detection in stack images: a persistent homology interpretation
This work addresses the need for automated and reliable neuron recognition in life sciences, which is incremental as it applies an existing mathematical tool to a specific domain.
The paper tackled the problem of automating neuron detection in biomedical images by developing algorithms based on persistent homology, resulting in an ImageJ plugin called NeuronPersistentJ that was experimentally validated for reliability.
Automation and reliability are the two main requirements when computers are applied in Life Sciences. In this paper we report on an application to neuron recognition, an important step in our long-term project of providing software systems to the study of neural morphology and functionality from biomedical images. Our algorithms have been implemented in an ImageJ plugin called NeuronPersistentJ, which has been validated experimentally. The soundness and reliability of our approach are based on the interpretation of our processing methods with respect to persistent homology, a well-known tool in computational mathematics.