Classification of unlabeled cells using lensless digital holographic images and deep neural networks

Image-based cell analytic methodologies offer a relatively simple and economical way to analyze and understand cell heterogeneities and developments. Owing to developments in high-resolution image sensors and high-performance computation processors, the emerging lensless digital holography technique enables a simple and cost-effective approach to obtain label-free cell images with a large field of view and microscopic spatial resolution.

The holograms of three types of cells, including MCF-10A, EC-109, and MDA-MB-231 cells, were recorded using a lensless digital holography system composed of a laser diode, a sample stage, an image sensor, and a laptop computer. The amplitude images were reconstructed using the angular spectrum method, and the sample to sensor distance was determined using the autofocusing criteria based on the sparsity of image edges and corner points. Four convolutional neural networks (CNNs) were used to classify the cell types based on the recovered holographic images.

Classification of two cell types and three cell types achieved an accuracy of higher than 91% by all the networks used. The ResNet and the DenseNet models had similar classification accuracy of 95% or greater, outperforming the GoogLeNet and the CNN-5 models.

These experiments demonstrated that the CNNs were effective at classifying two or three types of tumor cells. The lensless holography combined with machine learning holds great promise in the application of stainless cell imaging and classification, such as in cancer diagnosis and cancer biology research, where distinguishing normal cells from cancer cells and recognizing different cancer cell types will be greatly beneficial.