Low-Energy Femtosecond Laser Lens Fragmentation Techniques for Cataract.

Femtosecond laser has become an important tool in cataract surgery due to its precision and minimally invasive nature. However, the strong scattering properties of the lens limit surgical efficiency, especially when it comes to effectively cutting highly opaque and hard cataracts with femtosecond laser. This study proposes a method for achieving optical transparency in opaque lenses through tartrazine treatment.

Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain.

Single-neuron axonal projections reveal the route map of neuron output and provide a key cue for understanding how information flows across the brain. Reconstruction of single-neuron axonal projections requires intensive manual operations in tens of terabytes of brain imaging data and is highly time-consuming and labor-intensive. The main issue lies in the need for precise reconstruction algorithms to avoid reconstruction errors, yet current methods struggle with densely distributed axons, focusing mainly on skeleton extraction.

Non-invasive measurement of in vivo corneal steady-state biomechanical properties via controllable negative pressure inflation.

Objective: To measure the steady-state corneal biomechanical properties related to postoperative corneal ectasia, keratoconus, glaucoma and other ophthalmic diseases, we propose a novel in vivo measurement method.

Methods: By precisely manipulating ambient negative pressure via a suction device to achieve controlled in vivo corneal inflation, we analyzed the coupling relationship between corneal deformation response and negative pressure loading. The displacement corresponding to the corneal initial configuration under 0 mmHg was extrapolated.

AI-assisted cervical cytology precancerous screening for high-risk population in resource-limited regions using a compact microscope.

Insufficient coverage of cervical cytology screening in resource-limited areas remains a major bottleneck for women's health, as traditional centralized methods require significant investment and many qualified pathologists. Using consumer-grade electronic hardware and aspherical lenses, we design an ultra-low-cost and compact microscope. Given the microscope's low resolution, which hinders accurate identification of lesion cells in cervical samples, we train a coarse instance classifier to screen and extract feature sequences of the top 200 instances containing potential lesions from a slide.

Evaluating the performance of large language & visual-language models in cervical cytology screening.

Large language models (LLMs) and large visual-language models (LVLMs) have exhibited near-human levels of knowledge, image comprehension, and reasoning abilities, and their performance has undergone evaluation in some healthcare domains. However, a systematic evaluation of their capabilities in cervical cytology screening has yet to be conducted. Here, we constructed CCBench, a benchmark dataset dedicated to the evaluation of LLMs and LVLMs in cervical cytology screening, and developed a GPT-based semi-automatic evaluation pipeline to assess the performance of six LLMs (GPT-4, Bard, Claude-2.

High-precision angle error compensation method for a Dove prism scanning system based on galvanometers.

Dove prisms suffer from angle and shift errors due to inevitable errors in manufacturing and installation, limiting their applicability in tasks requiring high-precision scanning. These errors, particularly angle errors, can significantly deform and ruin the intended scanning trajectory. Here, we propose a method for compensating the angle errors in Dove prisms using galvanometers.

Multi-scale window transformer for cervical cytopathology image recognition.

Cervical cancer is a major global health issue, particularly in developing countries where access to healthcare is limited. Early detection of pre-cancerous lesions is crucial for successful treatment and reducing mortality rates. However, traditional screening and diagnostic processes require cytopathology doctors to manually interpret a huge number of cells, which is time-consuming, costly, and prone to human experiences.

Non-Gaussian anomalous diffusion of optical vortices.

Anomalous diffusion of different particlelike entities, the deviation from typical Brownian motion, is ubiquitous in complex physical and biological systems. While optical vortices move randomly in evolving speckle fields, optical vortices have only been observed to exhibit pure Brownian motion in random speckle fields. Here we present direct experimental evidence of the anomalous diffusion of optical vortices in temporally varying speckle patterns from multiple-scattering viscoelastic media.

Ten-kilohertz two-photon microscopy imaging of single-cell dendritic activity and hemodynamics .

Significance: The studying of rapid neuronal signaling across large spatial scales in intact, living brains requires both high temporal resolution and versatility of the measurement device.

Aim: We introduce a high-speed two-photon microscope based on a custom-built acousto-optic deflector (AOD). This microscope has a maximum line scan frequency of 400 kHz and a maximum frame rate of 10,000 frames per second (fps) at .

Multiparameter mobile blood analysis for complete blood count using contrast-enhanced defocusing imaging and machine vision.

Blood analysis through complete blood count is the most basic medical test for disease diagnosis. Conventional blood analysis requires bulky and expensive laboratory facilities and skilled technicians, limiting the universal medical use of blood analysis outside well-equipped laboratory environments. Here, we propose a multiparameter mobile blood analyzer combined with label-free contrast-enhanced defocusing imaging (CEDI) and machine vision for instant and on-site diagnostic applications.

Cervical cytopathology image refocusing via multi-scale attention features and domain normalization.

Cervical cytopathology image refocusing is important for addressing the problem of defocus blur in whole slide images. However, most of current deblurring methods are developed for global motion blur instead of local defocus blur and need a lot of supervised re-training for unseen domains. In this paper, we propose a refocusing method for cervical cytopathology images via multi-scale attention features and domain normalization.

Super-resolution Segmentation Network for Reconstruction of Packed Neurites.

Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in brain research. However, the difficulty in reconstructing dense neurites, wherein massive labor is required for accurate reconstruction in most cases, has not been well resolved. In this work, we provide a new pathway for solving this challenge by proposing the super-resolution segmentation network (SRSNet), which builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution (HR) space.

Brain-wide projection reconstruction of single functionally defined neurons.

Reconstructing axonal projections of single neurons at the whole-brain level is currently a converging goal of the neuroscience community that is fundamental for understanding the logic of information flow in the brain. Thousands of single neurons from different brain regions have recently been morphologically reconstructed, but the corresponding physiological functional features of these reconstructed neurons are unclear. By combining two-photon Ca imaging with targeted single-cell plasmid electroporation, we reconstruct the brain-wide morphologies of single neurons that are defined by a sound-evoked response map in the auditory cortices (AUDs) of awake mice.

Large depth-of-field fluorescence microscopy based on deep learning supported by Fresnel incoherent correlation holography.

Fluorescence microscopy plays an irreplaceable role in biomedicine. However, limited depth of field (DoF) of fluorescence microscopy is always an obstacle of image quality, especially when the sample is with an uneven surface or distributed in different depths. In this manuscript, we combine deep learning with Fresnel incoherent correlation holography to describe a method to obtain significant large DoF fluorescence microscopy.

Minimizing Probability Graph Connectivity Cost for Discontinuous Filamentary Structures Tracing in Neuron Image.

Neuron tracing from optical image is critical in understanding brain function in diseases. A key problem is to trace discontinuous filamentary structures from noisy background, which is commonly encountered in neuronal and some medical images. Broken traces lead to cumulative topological errors, and current methods were hard to assemble various fragmentary traces for correct connection.

StainNet: A Fast and Robust Stain Normalization Network.

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application.

Cross-Streams Through the Ventral Posteromedial Thalamic Nucleus to Convey Vibrissal Information.

Whisker detection is crucial to adapt to the environment for some animals, but how the nervous system processes and integrates whisker information is still an open question. It is well-known that two main parallel pathways through Ventral posteromedial thalamic nucleus (VPM) ascend to the barrel cortex, and classical theory suggests that the cross-talk from trigeminal nucleus interpolaris (Sp5i) to principal nucleus (Pr5) between the main parallel pathways contributes to the multi-whisker integration in barrel columns. Moreover, some studies suggest there are other cross-streams between the parallel pathways.

Foreground Estimation in Neuronal Images With a Sparse-Smooth Model for Robust Quantification.

3D volume imaging has been regarded as a basic tool to explore the organization and function of the neuronal system. Foreground estimation from neuronal image is essential in the quantification and analysis of neuronal image such as soma counting, neurite tracing and neuron reconstruction. However, the complexity of neuronal structure itself and differences in the imaging procedure, including different optical systems and biological labeling methods, result in various and complex neuronal images, which greatly challenge foreground estimation from neuronal image.

Expansion tomography for large volume tissue imaging with nanoscale resolution.

Expansion microscopy enables conventional diffraction limit microscopy to achieve super-resolution imaging. However, the enlarged tissue lacks an objective lens with sufficient working distance that can image tissues with whole-brain-scale coverage. Here, we present expansion tomography (ExT) to solve this problem.

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice.