Biology subjects

Rui, C.

Publications and source records attributed to Rui, C..

3 recordsLinked to original sources

bioRxiv2026

A foundation AI model enhances electron microscopy image analysis

Electron microscopy (EM) and advanced volume EM have extensive applications in deciphering cellular ultrastructures for life sciences. However, quality issues of EM images significantly impede the precise analysis and discovery of nanoscale biological structures. Here, we present an unsupervised Foundation Model for Five Tasks (DF5T) model for image enhancement through denoising, deblurring, super-resolution, two-dimensional (2D) inpainting, and three-dimensional (3D) isotropic restoration. DF5T was trained on an extensive dataset of multi-source membrane-bound organelle EM images, consisting of a total of over 2.25 million images. On unseen data, DF5T outperformed the existing state-of-the-art models in all five tasks. Furthermore, DF5T substantially compensates for missing three-dimensional structural information and reveals significant ultrastructural alterations in organelle geometry following chemical treatment. We demonstrate that DF5T significantly enhances electron microscopy images quality by improving the accuracy of downstream organelle segmentation and 3D structural restoration, thereby contributing to future advances in biological research.

cell biology↗

bioRxiv2025

EMCF ecosystem: Towards pretrained foundation model for electron microscopy image analysis

Volume electron microscopy (vEM) enables nanoscale visualization of three-dimensional (3D) cellular ultrastructure, providing critical insights into physiological processes and pathological alterations. However, its application to large-scale biological tissues remains constrained by two major bottlenecks: prolonged image acquisition and inefficient data processing. Here, we present EMCF ecosystem (EMCFsys), an integrated ecosystem designed to overcome these challenges through three key components: a large-scale benchmark dataset (EMCFD) comprising 4,002,802 high-quality images across 14 EM modalities and 6 biological kingdoms; a foundation image restoration model (EMCellFiner); and a scalable image analysis foundation model (EMCellFound). Together, these modules systematically enhance image quality and substantially improve analysis efficiency. Our results show that EMCellFiner outperforms specialist models in restoring degraded images, even surpassing original ground truth sharpness in certain artifact regions, and reduces imaging time by 16-fold by enabling low-resolution and low dwell time acquisition. EMCellFound exhibits exceptional feature discriminability, outperforms specialist models in classification, semantic segmentation and instance segmentation. It also enables high-precision 3D reconstruction of organelles (e.g., endoplasmic reticulum) with minimal labeled data (0.01% of total volume). We validated the EMCFsys on unseen datasets across diverse biological contexts and imaging platforms. By publicly releasing both the dataset and models, we establish a scalable paradigm for automated, high-throughput vEM data interpretation, accelerating exploration of lifes nanoscale structure and function across biology.

cell biology↗

bioRxiv2020

Morphologic Classification and Automatic Nugent Scoring of Bacterial Vaginosis by Deep Neural Networks

BackgroundBacterial vaginosis (BV) was the most common condition for womens health caused by the disruption of normal vaginal flora and an overgrowth of certain disease-causing bacteria, affecting 30-50% of women at some time in their lives. Gram stain followed by Nugent scoring (NS) based on bacterial morphotypes under the microscope was long considered golden standard for BV diagnosis. This conventional manual method was often considered labor intensive, time consuming, and variable results from person to person. MethodsWe developed four convolutional neural networks (CNN) models, and evaluated their ability to automatic identify vaginal bacteria and classify Nugent scores from microscope images. All the CNN models were first trained with 23280 microscopic images labeled with Nugent scores from top experts. A separate set of 5815 images were evaluated by the CNN models. The best CNN model was selected to generalize its application on an independent sets of 1082 images collecting from three teaching hospitals. Different hardwares were used to take images in hospitals. ResultsOur model could classify three Nugent Scores from images with high three classification accuracy of 89.3% (with 82.4% sensitivity and 96.6% specificity) on the 5815 test images, which was better diagnostic yield than the top-level technologists and obstetricians in China. The ability of generalization for our model was strong that it obtained 75.1%, which was 6.6% higher than the average of technologists. ConclusionThe CNN model over performed human healthcare practitioners on accuracy, efficiency and stability for BV diagnosis using microscopic image-based Nugent scores. The deep learning model may offer translational application in automating diagnosis of bacterial vaginosis with proper supporting hardware.

microbiology↗