Autofocusing is a critical step for high-quality microscopic imaging of specimens, especially
for measurements that extend over time covering large fields of view. Autofocusing is
generally practiced using two main approaches. Hardware-based optical autofocusing
methods rely on additional distance sensors that are integrated with a microscopy system.
Algorithmic autofocusing methods, on the other hand, regularly require axial scanning
through the sample volume, leading to longer imaging times, which might also introduce …

(57) ABSTRACT A deep learning-based offline autofocusing method and system is
disclosed herein, termed a Deep-R trained neural network, that is trained to rapidly and
blindly autofocus a single-shot microscopy image of a sample or specimen that is acquired
at an arbitrary out-of-focus plane. The eficacy of Deep-R is illustrated using various tissue
sections that were imaged using fluorescence and brightfield microscopy modalities and
demonstrate single snapshot autofocusing under different scenarios, such as a uniform axial …