Autofocus of whole slide imaging based on convolution and recurrent neural networks

During the process of whole slide imaging, it is necessary to focus thousands of fields of view to obtain a high-quality image. To make the focusing procedure efficient and effective, we propose a novel autofocus algorithm for whole slide imaging. It is based on convolution and recurrent neural networks to predict the out-of-focus distance and subsequently update the focus location of the camera lens in an iterative manner. More specifically, we train a convolution neural network to extract focus information in the form of a focus feature vector. In order to make the prediction more accurate, we apply a recurrent neural network to combine focus information from previous search iteration and current search iteration to form a feature aggregation vector. This vector contains more focus information than the previous one and is subsequently used to predict the out-of-focus distance. Our experiments indicate that our proposed autofocus algorithm is able to rapidly determine the optimal in-focus image.

Automated summary

The proposed autofocus algorithm for whole slide imaging utilizes convolution and recurrent neural networks to predict the out-of-focus distance and update the focus location of the camera lens iteratively, achieving rapid determination of the optimal in-focus image.