Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears

Boundary-artifact-free quantitative phase imaging with Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) enables high-resolution, wide-field imaging of both amplitude and phase, presenting significant potential for applications in digital pathology and cell biology. However, artifacts commonly observed at the boundaries of reconstructed images can significantly degrade imaging quality and phase retrieval accuracy. These boundary artifacts are typically attributed to the use of the fast Fourier transform (FFT) on non-periodic images. Another significant physical factor that should not be overlooked is the transverse diffraction of light across boundaries. Here, we introduce a boundary extension reconstruction framework for FPM, termed BE-FPM, which provides boundary-artifact-free quantitative phase imaging (QPI) with minimal computational overhead. In this method, the reconstructed image is initialized with zero-padding and then self-extrapolated during the subsequent iterative reconstruction process. This approach allows for partial restoration of the sample beyond the boundary, ensuring sample consistency around the boundary and addressing the boundary artifact problem fundamentally. We demonstrate the effectiveness of the proposed BE-FPM on both microlens array and live cells, establishing it as an effective FPM solver for boundary-artifact-free QPI and accurate phase characterization for various types of samples.

Application of image recognition technology in pathological diagnosis of blood smears

Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.

Fourier Ptychographic Microscopy 10 Years on: A Review

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.