Open AccessDeep learning-based single-shot autofocus method for digital microscopy
Author(s) -
Jun Liao,
Xu Chen,
Ge Ding,
Pei Dong,
Ye Hu,
Han Wang,
Yongbing Zhang,
Jianhua Yao
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.446928
Subject(s) - autofocus , computer science , artificial intelligence , computer vision , digital pathology , focus (optics) , microscopy , microscope , depth of field , image quality , optics , image (mathematics) , physics
Digital pathology is being transformed by artificial intelligence (AI)-based pathological diagnosis. One major challenge for correct AI diagnoses is to ensure the focus quality of captured images. Here, we propose a deep learning-based single-shot autofocus method for microscopy. We use a modified MobileNetV3, a lightweight network, to predict the defocus distance with a single-shot microscopy image acquired at an arbitrary image plane without secondary camera or additional optics. The defocus prediction takes only 9 ms with a focusing error of only ∼1/15 depth of field. We also provide implementation examples for the augmented reality microscope and the whole slide imaging (WSI) system. Our proposed technique can perform real-time and accurate autofocus which will not only support pathologists in their daily work, but also provide potential applications in the life sciences, material research, and industrial automatic detection.