Nonmechanical parfocal and autofocus features based on wave propagation distribution in lensfree holographic microscopy

Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications

The need for faster and more accessible alternatives to laboratory microscopy is driving many innovations throughout the image and data acquisition chain in the biomedical field. Benchtop microscopes are bulky, lack communications capabilities, and require trained personnel for analysis. New technologies, such as compact 3D-printed devices integrated with the Internet of Things (IoT) for data sharing and cloud computing, as well as automated image processing using deep learning algorithms, can address these limitations and enhance the conventional imaging workflow. This review reports on recent advancements in microscope miniaturization, with a focus on emerging technologies such as photoacoustic microscopy and more established approaches like smartphone-based microscopy. The potential applications of IoT in microscopy are examined in detail. Furthermore, this review discusses the evolution of image processing in microscopy, transitioning from traditional to deep learning methods that facilitate image enhancement and data interpretation. Despite numerous advancements in the field, there is a noticeable lack of studies that holistically address the entire microscopy acquisition chain. This review aims to highlight the potential of IoT and artificial intelligence (AI) in combination with portable microscopy, emphasizing the importance of a comprehensive approach to the microscopy acquisition chain, from portability to image analysis.

Digital in-line holographic microscopy for label-free identification and tracking of biological cells

Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments.

Lens-free auto-focusing imaging algorithm for the ultra-broadband light source

Auto-focusing is an essential task for lens-free holographic microscopy, which has developed many methods for high precision or fast refocusing. In this work, we derive the relationship among intensity derivation, the derivative of spectral distribution, as well as the distribution of the object, and propose a new auto-focusing criterion, the Robert critical function with axial difference (RCAD), to enhance the accuracy of distance estimation for lens-free imaging with the ultra-broadband light source. This method consists of three steps: image acquisition and preprocessing, axial-difference calculation, and distance estimation with sharpness analysis. The simulations and experiments demonstrate that the accuracy of this metric on auto-focusing with the ultra-broadband spectrum can effectively assist in determining the off-focus distance. The experiments are conducted in an ultra-broad-spectrum on-chip system, where the samples including the resolution target and the cross-section of the Tilia stem are employed to maximize the applicability of this method. We believe that the RCAD criterion is expected to be a useful auxiliary tool for lens-free on-chip microscopes with ultra-broadband spectrum illumination.