This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

A lateral shearing interferometer is used for direct holographic imaging of microorganisms. This is achieved by increasing the shear to be larger than the object size and results in a very simple and inexpensive common-path imaging device that can be easily coupled to the output of an inverted microscope. The shear is created by reflections from the front and back surface of a glass plate. Stability measurements show a standard deviation of the phase measurements of less than 1nm over 8 min. without any vibration compensation. The setup is applied to imaging both microorganisms in a microfluidic channel and red blood cells and reconstructions are presented.

Digital holographic microscopy (DHM) is one of the most effective techniques used for quantitative phase imaging of cells. Here we present a compact, easy to implement, portable, and very stable DHM setup employing a self-referencing Lloyd’s mirror configuration. The microscope is constructed using a diode laser source and a CMOS sensor, making it cost effective. The reconstruction of recorded holograms yields the amplitude and phase information of the object. The temporal stability of the presented technique was found to be around 0.9 nm without any vibration compensation, which makes it ideal for studying cell profile changes. This aspect of the technique is demonstrated by studying membrane fluctuations of red blood cells.

Diagnosis of malaria is important for its medication and treatment. An automatic compact diagnostic tool will be advantageous especially for healthcare personnel working in developing countries, which lack trained professionals and high-quality equipments. Digital holographic microscopy is one of the advanced techniques for quantitative evaluation of cells, providing cell thickness information directly. This information obtained by interferometric comparison can be used to identify as well as to compare cells. Here, we describe the use of digital holographic interferometric microscopy (DHIM) with numerical focusing for automatic identification of malaria-infected red blood cells (RBCs). Identification is done by comparing its shape profile with that of a healthy RBC. A correlation function was used to separate healthy and malaria-infected RBCs.

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

We demonstrate a successful deep learning strategy for cell identification and disease diagnosis using spatio-temporal cell information recorded by a digital holographic microscopy system. Shearing digital holographic microscopy is employed using a low-cost, compact, field-portable and 3D-printed microscopy system to record video-rate data of live biological cells with nanometer sensitivity in terms of axial membrane fluctuations, then features are extracted from the reconstructed phase profiles of segmented cells at each time instance for classification. The time-varying data of each extracted feature is input into a recurrent bi-directional long short-term memory (Bi-LSTM) network which learns to classify cells based on their time-varying behavior. Our approach is presented for cell identification between the morphologically similar cases of cow and horse red blood cells. Furthermore, the proposed deep learning strategy is demonstrated as having improved performance over conventional machine learning approaches on a clinically relevant dataset of human red blood cells from healthy individuals and those with sickle cell disease. The results are presented at both the cell and patient levels. To the best of our knowledge, this is the first report of deep learning for spatio-temporal-based cell identification and disease detection using a digital holographic microscopy system.