Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.

ABSTRACT: The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM repo

SUBMITTER: Cai Y 

PROVIDER: S-EPMC8462685 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature