Implementation of deep neural networks to count dopamine neurons in substantia nigra.

ABSTRACT: Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time-consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neurons. Implementation of a programming paradigm enables a computer to learn from the data and development of an automated cell counting method. The advantages of computerized counting are reproducibility, elimination of human error and fast high-capacity analysis. We implemented whole-slide digital imaging and deep convolutional neural networks (CNN) to count substantia nigra dopamine neurons. We comp

SUBMITTER: Penttinen AM 

PROVIDER: S-EPMC6585833 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature