Whole slide imaging system using deep learning-based automated focusing

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.

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

“…[32][33][34] ML can use these techniques to analyze whole slides with automated focusing. 35 Classification of leukemia subtypes (AML, acute lymphoblastic leukemia, chronic myeloid leukemia, and chronic lymphocytic leukemia) can be achieved by a variety of ML approaches such as DNN, 36 SVM, and k-means-clustering (an unsupervised ML technique in which similar data points are grouped into k clusters according to their distance to a cluster mean). 37,38 Another essential part of the diagnostic process in AML is flow cytometry, 39 which can aid in the detection of relapse with a higher sensitivity than cytomorphology alone.…”

Autofocusing technologies for whole slide imaging and automated microscopy

“…Deep learning has been demonstrated as a powerful tool for solving inverse problems. With the advent of accelerated computing and deep learning frameworks such as TensorFlow and PyTorch, researchers have also explored various deep learning-based solutions for autofocusing [21,92,[139][140][141][142][143][144][145][146][147][148][149][150]. As shown in Figure 11, the reported deep-learning solutions can be, in general, categorized into two groups.…”

“…The first group is to predict the defocus distance or to locate the out‐of‐focus regions based on one or more input defocused images [21, 92–94, 140, 141, 144, 146, 147, 149, 150]. For example, Jiang et al employed a convolutional neural network (CNN) to estimate the defocus distance based on the transform‐ and multi‐domain inputs [141].…”

Optical Technologies for Improving Healthcare in Low-Resource Settings: introduction to the feature issue

“…Some articles in this feature issue also highlight how recent advances in machine learning can be leveraged to enable low-cost optical technologies to provide compelling solutions to healthcare challenges. The whole slide imaging system described by Rai et al [12] uses deep learning to enable automated focusing of microscopy data that is comparable to the natural ability of human operators. In Haeffele et al [13], a lens-free microscope is augmented by a convolutional neural network trained for platelet detection.…”