A Miniaturized and Intelligent Lensless Holographic Imaging System With Auto-Focusing and Deep Learning-Based Object Detection for Label-Free Cell Classification

Abstract

Cell detection and classification is a key technique for disease diagnosis, but conventional methods such as optical microscopy and flow cytometry have limitations in terms of field-of-view (FOV), throughput, cost, size, and operation complexity. Lensless holographic imaging is a promising alternative that offers large FOV, rich information content, and simple structure. However, its performance on cell detection and classification still needs to be improved. In this paper, we propose an intelligent cell detection system based on lensless holographic imaging and deep learning. Our system uses unstained cells suspended in solution as samples and employs a threshold segmentation-based auto-focusing algorithm to determine the optimal focusing distance for each imaging session. We also use a deep learning-based object detection neural network to classify different types of cells from the focused holographic images without the need for cell segmentation. We demonstrated the performance of our system using four cell detection tasks: tumor cells vs. polystyrene microspheres (77.6% accuracy), different tumor cells (80.1% accuracy), red blood cells vs. white blood cells (78.1% accuracy), white blood cell subtypes (88% accuracy), which showed that our system achieved high accuracy with label-free, portable, intelligent, and fast cell detection capabilities. It has potential applications in the miniaturized cell detection field.

Keywords

• Auto-focusing algorithm
• deep learning
• lensless holographic imaging
• object detection and classification
• label-free