Abstract
The auto focusing system, which involves moving a microscope stage along a vertical axis to find an optimal focus position, is the chief component of an automated digital microscope. Current automated focusing algorithms, especially those deployed in cost effective microscopy systems, often cannot match the efficiency of a skilled human operator in keeping a sample in focus. This work presents an auto focusing system that utilises the recent advances in machine learning, namely deep convolutional neural networks (CNN). It improves upon prior work in this domain. The results of the focusing algorithm are demonstrated on an open data set. We describe the practical implementation of this method on a low cost digital microscope to create a whole slide imaging system (WSI). Results of a clinical study using this WSI system are presented. The study demonstrates the efficacy of this system in a practical scenario.
Highlights
Diagnosis of diseases using manual microscopic review is still a gold standard in many areas
The review of the digital images led to the identification of certain organisms which were not detected in manual review
This paper presented a new method for applying deep learning for focus distance estimation in automated digital microscopy
Summary
Diagnosis of diseases using manual microscopic review is still a gold standard in many areas. Known as whole slide imaging (WSI) systems, partially automate the process of review. They capture digital images of the physical slide and create a “virtual slide”. This virtual slide can be reviewed by multiple experts, enabling both remote review and collaborative review, and opens up the possibility of automated analysis by artificial intelligence (AI) systems [1,2]. Research on automated focusing has been pursued for a few decades [3]
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