Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets.

ABSTRACT: Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies. These methods enable unprecedented studies of life at the microscale, but bring with them larger and more complex datasets. New image processing techniques are therefore called for to analyze the resulting images in an accurate and efficient manner. Convolutional neural networks are becoming the standard for classification of objects within images due to their accuracy and generalizability compared to traditional techniques. Their application to data derived from 3D imaging, however, is relatively new and has mostly been in areas of magnetic resonance imaging and computer tomography. It remains unclear

SUBMITTER: Hay EA 

PROVIDER: S-EPMC6292638 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature