Improving cellular analysis throughput of lens-free holographic microscopy with circular Hough transform and convolutional neural networks

Abstract

In recent years, lens-free Digital In-Line Holographic Microscopy (DIHM) has become an essential tool for in-vitro experiments. Specifically, it offers many innovative approaches to the characterization of cancer cell assays, which are pioneering studies in the prevention and treatment of cancer, the most common disease of our time. With DIHM, images with very high resolution, high magnification coefficient, and wide FOV can be obtained. However, the success of the DIHM is directly dependent on the successful coordination of the imaging and analysis processes. This study proposes a combined approach of circular Hough transform (CHT) and Convolutional Neural Network (CNN) algorithms to improve the cellular analysis aspect of DIHM. The design, installation, and testing of the DIHM for cellular imaging are presented. Resolution analyses of the microscopy system are presented using a standard USAF 1951 resolution slide. MCF-7 cell lines were imaged with the designed microscope at various concentrations. CHT was used in the wide search space to detect the particles in the cell slides. The proposed method detected all overlapping cells and non-cell particles in the images. CNN was used to classify particles as living, dead, and noncell cells. A GPU-based GUI has been designed to perform imaging and analysis operations. The performance of the proposed method is statistically compared with a reference system using various performance metrics. The accuracy of the model is calculated as %99.92 and also the results obtained with the proposed method show more than R2=0.99 correlation with a reference system. The results of the proposed method have improved the analytical performance of DIHM in in-vitro cell cancer cell line experiments. In addition, the method contributes to the analysis of high-resolution images.