Journal of Innovative Optical Health Sciences最新文献

In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment, which renders it a valuable tool for both scientific research and clinical applications. However, the conventional approach for improving classification accuracy often involves labeling cells with fluorescence, which can lead to potential phototoxicity. This study proposes a label-free in-vivo flow cytometry technique, called dynamic YOLOv4 (D-YOLOv4), which improves classification accuracy by integrating absorption intensity fluctuation modulation (AIFM) into YOLOv4 to demodulate the temporal features of moving red blood cells (RBCs) and platelets. Using zebrafish as an experimental model, the D-YOLOv4 method achieved average precisions (APs) of 0.90 for RBCs and 0.64 for thrombocytes (similar to platelets in mammals), resulting in an overall AP of 0.77. These scores notably surpass those attained by alternative network models, thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivo flow cytometry, which holds promise for diverse in-vivo cell classification applications.

The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.

Polarimetry is a powerful optical tool in the biomedical field, providing more comprehensive information on the sub-wavelength micro-physical structure of a sample than traditional light intensity measurement techniques. This review summarizes the concepts and techniques of polarization and its biomedical applications. Specifically, we first briefly describe the basic principles of polarized light and the Mueller matrix (MM) decomposition method, followed by some research progress of polarimetric measurement techniques in recent years. Finally, we introduce some studies on biological tissues and cells, and then illustrate the application value of polarization optical method.

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media, particularly for measurements relevant to near-infrared spectroscopy. The three temporal domains, continuous wave, frequency domain, and time domain, each obtain different optical data types whose changes may be related to effective homogeneous changes in the absorption coefficient. Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change. Therefore, spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data types in the three temporal domains. The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media. These works are experimental and theoretical, presenting one-, two-, and three-dimensional sensitivity maps for different Near-Infrared Spectroscopy methods, domains, and data types. Following this history, we present a compendium of sensitivity maps organized by temporal domain and then data type. This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document. Methods for one to generate these maps are provided in Appendix A, including the code. This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize, investigate, compare, and generate sensitivity to localized absorption change maps.