Transform- and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging.

ABSTRACT: A whole slide imaging (WSI) system has recently been approved for primary diagnostic use in the US. The image quality and system throughput of WSI is largely determined by the autofocusing process. Traditional approaches acquire multiple images along the optical axis and maximize a figure of merit for autofocusing. Here we explore the use of deep convolution neural networks (CNNs) to predict the focal position of the acquired image without axial scanning. We investigate the autofocusing performance with three illumination settings: incoherent Kohler illumination, partially coherent illumination with two plane waves, and one-plane-wave illumination. We acquire ~130,000 images with different defocus distances as the training data set. Different defocus distances lead to different spatial fea

SUBMITTER: Jiang S 

PROVIDER: S-EPMC5905909 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature