Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks.

ABSTRACT: Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based o

SUBMITTER: Banerjee S 

PROVIDER: S-EPMC8486300 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature