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

Banerjee, Samik, Magee, Lucas, Wang, Dingkang, Li, Xu, Huo, Bing-Xing, Jayakumar, Jaikishan, Matho, Katherine, Lin, Meng-Kuan, Ram, Keerthi, Sivaprakasam, Mohanasankar, Huang, Josh, Wang, Yusu, Mitra, Partha P (October 2020) Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. Nature Machine Intelligence, 2 (10). 585-+. ISSN 2522-5839

DOI: 10.1038/s42256-020-0227-9

Abstract

Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods that 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 substantial 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 on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (for example, connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision and recall close to 90% compared with human observers. We have adapted our architecture to a high-performance pipeline capable of semantic segmentation of light-microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

Item Type: Paper
Subjects: bioinformatics > computational biology > algorithms > machine learning
Investigative techniques and equipment > Whole Brain Circuit Mapping
CSHL Authors:
Communities: CSHL labs > Huang lab
CSHL labs > Mitra lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: October 2020
Date Deposited: 06 May 2021 14:01
Last Modified: 06 May 2021 14:01
Related URLs:
URI: https://repository.cshl.edu/id/eprint/39999

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving