Journal of Biophotonics最新文献

Scleral plaque spots and pigmentation dots have significant potential for non-invasive disease diagnosis. However, the high intra-class variability in their size, shape, and distribution across individuals poses a critical challenge for automated and robust detection in complex scleral images. This study presents an integrated system that combines sclera imaging and object detection to overcome these challenges. First, a specially designed sclera imaging device is employed to obtain the high quality and shadowless scleral images. Then, an improved YOLOv5 model is proposed, incorporating three key enhancements: a CBAM module to suppress redundant information, a series of Mamba blocks to expand the model's receptive field in scleral images, and a small head strategy to enhance the detection effectiveness on small objects. The proposed detection algorithm was validated on three disparate scleral datasets. The experimental results demonstrate that the proposed method can effectively detect scleral spots and dots, featuring robust and generalizable performance.

The depth of field limitations of the microscope optical system make it impossible for a single image to clearly represent the structure of the sample at different depths simultaneously. While multifocal fusion integrates sharp areas from multiple focal planes to achieve wide-field high-resolution imaging, current methods have edge artifacts due to poor background suppression and inadequate edge enhancement. To effectively alleviate this key challenge, a multifocal micro-image fusion method based on background optimization and edge enhancement is proposed. The method first performs saturation-based background optimization processing on microscopic images, and then uses an edge enhancement algorithm and an adjacency filter to extract the decision maps and infographics corresponding to the structural layer and detail layer, respectively. Experimental results show that the proposed method has significant advantages in subjective and quantitative evaluation, and can improve edge information transfer (Q^AB/F) by up to 30% while reducing edge artifacts.

Spectral imaging extends the capabilities of conventional microscopy by incorporating spectral information into spatially resolved images, enabling enhanced characterization of biological specimens. This paper presents a multimodal microscopic spectral imaging (MmSI) system that integrates spectral and temporal-spectral scanning for comprehensive microscopic analysis. The MmSI system employs a liquid crystal tunable filter (LCTF) to achieve flexible spectral selection and high spatial resolution, facilitating transmission hyperspectral imaging (THSI), fluorescence hyperspectral imaging (FHSI), and multi-wavelength time-lapse imaging. A custom-developed graphical user interface synchronizes the LCTF and CCD camera for efficient acquisition of spatial, spectral, and temporal-spectral data. System calibration and characterization in the spatial, spectral, and temporal domains, including linearity assessment with respect to exposure time and emission intensity, confirm its quantitative accuracy. The effectiveness and reliability of the system are demonstrated through validation experiments conducted on model systems, demonstrating its versatility for advanced biomedical research, particularly in the investigation of dynamic biological processes.

The sensitivity of the photoacoustic effect to hemoglobin and structural features in biological tissues has led to its wide application in biomedical research, including vascular imaging and tumor tissue detection. In this study, we customed an optical-resolution photoacoustic microscopy system to analyze tissue sections derived from mixed hemorrhoids and ileocecal malignant tumors. Experimental results show that photoacoustic microscopy system accurately captures tissue morphology, revealing the edges and microvessel networks in mixed hemorrhoids and the irregular boundaries and mass-like structures in ileocecal tumors. Furthermore, this study employed a morphological approach to quantitatively analyze specific regional features in colorectal tissue images. Experimental results demonstrate that photoacoustic microscopy can accurately capture the boundaries in hemorrhoid regions, providing clinical guidance for assessing hemorrhoid types. It can also distinguish ileocecal tumor tissue from healthy ileocecal tissue based on tumor boundary characteristics, showing significant potential to improve both clinical diagnostic accuracy and surgical resection efficiency.

Plasmonic nanobubbles (PNB) are on-demand transient vapor nanobubbles generated around laser pulse-heated plasmonic nanoparticles (NP). Despite promising in vivo tests, their clinical translation is delayed by complex lasers, bulky optical guides, and thermally fragile gold NPs with low PNB generation efficacy. In clinics, there is an unmet demand for in vivo real-time detection of microscopic cancers. Here, we resolve these limitations with an all-new combination of long and safe infrared laser pulses, small biocompatible titanium nitride (TiN) NPs for cancer targeting, and an optical fiber probe for minimally invasive PNB generation and detection in vivo. In water suspensions, tissue, and human lung cancer animal models, TiN NPs efficiently generated PNBs with 325 ps/1064 nm laser pulses. A PNB combination device instantly diagnosed lung cancer in animals with close to 100% sensitivity and specificity. The developed PNB combination device will support minimally invasive clinical applications for real-time high-sensitivity cancer diagnosis during biopsy and surgery.

Sperm morphology serves as a crucial indicator of fertilization potential; however, the fixation and staining required for its assessment irreversibly result in irreversible damage to sperm. Here, an improved Generative Adversarial Network (GAN) virtual staining model based on dark-field microscopy enables real-time conversion of label-free semen smears into high-contrast Papanicolaou-equivalent stained images. The experimental results demonstrated that our network completed virtual staining in ~0.047 s for a 2048 × 2048 image. Furthermore, the assessment showed that the mean squared error and the structural similarity between virtual staining and true Papanicolaou staining are 0.0044 ± 0.0031 and 0.905 ± 0.015, respectively. Our network bypasses the typically labor-intensive and costly histological staining procedures, enabling real-time, non-destructive virtual staining of motile spermatozoa without the need for laboratory quality control, and paves a novel way for selection of sperm for intracytoplasmic sperm injection (ICSI).

Blood cell counting is vital for medical diagnosis, and image recognition offers an automated approach. While most studies rely on microscopic images, these provide limited information. In contrast, multispectral imaging captures additional optical characteristics, improving the delineation of cellular boundaries and structures. This paper presents a blood cell recognition method based on multispectral imaging and YOLOv5. Blood cell images at five wavelengths were fused for multispectral information. The standard and modified YOLOv5 models were trained and tested on single-wavelength and multispectral images. Experimental results demonstrate that, compared with single-wavelength imaging, multispectral imaging markedly enhances the recognition performance of blood cells, yielding identification precision values of 99.9% for red blood cells and 96.1% for platelets. For white blood cells, which are relatively scarce, the recognition precision reached 98.9%, representing a 12.26% improvement over the best-performing single-wavelength model. Multispectral imaging shows significant potential for high-precision detection of rare cells.