bioRxiv preprint doi: https://doi.org/10.1101/445742; this version posted December 27, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Sparse Recovery Methods for Cell Detection and Layer Estimation Theodore J. LaGrow 1;∗, Michael G. Moore 1 Judy A. Prasad2, Alexis Webber3, Mark A. Davenport1, and Eva L. Dyer 1;3;∗ 1Georgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, GA, USA 2University of Chicago, Department of Neurobiology, Chicago, IL, USA 3Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA, USA Correspondence*: Theodore J. LaGrow, Eva L. Dyer
[email protected],
[email protected] ABSTRACT Robust methods for characterizing the cellular architecture (cytoarchitecture) of the brain are needed to differentiate brain areas, identify neurological diseases, and model architectural differences across species. Current methods for mapping the cytoarchitecture and, in particular, identifying laminar (layer) divisions in tissue samples require the expertise of trained neuroanatomists to manually annotate the various regions-of-interest and cells within an image. However, as neuroanatomical datasets grow in volume, manual annotations become inefficient, impractical, and risk the potential of biasing results. In this paper, we propose an automated framework for cellular detection and density estimation that enables the detection of laminar divisions within retinal and neocortical histology datasets. Our approach for layer detection uses total variation minimization to find a small number of change points in the density that signify the beginning and end of each layer.