Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology

Publication date: Available online 27 December 2018Source: Journal of Chemical NeuroanatomyAuthor(s): Saeed S. Alahmari, Dmitry Goldgof, Lawrence Hall, Hady Ahmady Phoulady, Raj H. Patel, Peter R. MoutonAbstractIn recent decades stereology-based studies have played a significant role in understanding brain aging and developing novel drug discovery strategies for treatment of neurological disease and mental illness. A major obstacle to further progress in a wide range of neuroscience sub-disciplines remains the lack of high-throughput technology for stereology analyses. Though founded on methodologically unbiased principles, commercially available stereology systems still rely on well-trained humans to manually count hundreds of cells within each region of interest (ROI). Even for a simple study with 10 controls and 10 treated animals, cell counts typically require over a month of tedious labor and high costs. Furthermore, these studies are prone to errors and poor reproducibility due to human factors such as subjectivity, variable training, recognition bias, and fatigue. Here we propose a deep neural network-stereology combination to automatically segment and estimate the total number of immunostained neurons on tissue sections. Our three-step approach consists of 1) creating extended-depth-of-field (EDF) images from z-stacks of images (disector stacks); 2) applying an adaptive segmentation algorithm (ASA) to label stained cells in the EDF images (i.e., create masks) for trai...
Source: Journal of Chemical Neuroanatomy - Category: Neuroscience Source Type: research