Rai Dastidar Tathagato, Ethirajan Renu
SigTuple Technologies, Bengaluru, Karnataka 560102, India.
Biomed Opt Express. 2019 Dec 23;11(1):480-491. doi: 10.1364/BOE.379780. eCollection 2020 Jan 1.
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.
自动聚焦系统是自动数字显微镜的主要组成部分，该系统通过沿垂直轴移动显微镜载物台来寻找最佳聚焦位置。当前的自动聚焦算法，尤其是那些应用于低成本显微镜系统中的算法，在使样本保持聚焦方面往往无法达到熟练操作人员的效率。这项工作提出了一种利用机器学习最新进展的自动聚焦系统，即深度卷积神经网络（CNN）。它改进了该领域先前的工作。在一个开放数据集上展示了聚焦算法的结果。我们描述了该方法在低成本数字显微镜上的实际应用，以创建一个全切片成像系统（WSI）。展示了使用该WSI系统的临床研究结果。该研究证明了该系统在实际场景中的有效性。
