Donglai Wei

RnR-ExM: Robust Non-rigid Registration Challenge for Expansion Microscopy Volumes

The paper presents a challenge for 3D registration of expansion microscopy volumes, evaluates top methods, and highlights future directions for ExM registration.

Simultaneous brain-wide single-cell recording resolves spatiotemporal memory architecture

The paper introduces GLOBE, a brain-wide single-cell recording technology, and demonstrates its use in capturing Fos transcriptional activity across a mouse brain over time.

NuGraph: Graph-Based Reasoning over 3D Primitives for Nucleus Segmentation Correction

This paper introduces a graph-based framework for correcting nucleus segmentation errors in 3D reconstructions, achieving high accuracy on brain-scale benchmarks.

VesiclePy: A Machine Learning Vesicle Analysis Toolbox for Volume Electron Microscopy

VesiclePy automates vesicle segmentation, classification, and spatial analysis in large-volume electron microscopy data, processing multiterabyte datasets and classifying vesicles into five types.

NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images

The paper develops NucleoNet and DropNet, deep learning models for segmenting nuclei and lipid droplets in EM images, and applies them to compare 2D/3D cancer models with in vivo tumors.

Multiplexed, scalable analog recording of gene regulation dynamics over weeks using intracellular protein tapes

This paper introduces CytoTape, a tool for continuous, multiplexed recording of gene regulation dynamics over weeks at single-cell resolution, and demonstrates its use in measuring transcription factor activities.

SynAnno: Interactive Guided Proofreading of Synaptic Annotations

SynAnno is an interactive tool that streamlines synaptic annotation proofreading in connectomics by integrating guided workflows and machine learning assistance, improving efficiency and reducing errors.

MitoEM 2.0: A Benchmark for Challenging 3D MitochondriaInstance Segmentation from EM Images

MitoEM 2.0 provides a benchmark for 3D mitochondria segmentation in EM images with diverse datasets and standardized labels for model evaluation and comparison.

Developmental Connectomics of the Mouse Cerebellum

This study investigates synaptic connectivity development in the mouse cerebellum, revealing sequential input-dependent wiring and individualized parallel fiber inputs to Purkinje cells.

A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain

The paper presents a connectomic dataset of a larval zebrafish brain, enabling circuit analysis through synaptic and neuronal annotations.

BiaPy: Accessible deep learning on bioimages

BiaPy provides an open-source library for deep learning in bioimage analysis, offering tools for 2D/3D image data with multi-GPU support and customizable workflows.

WormID-Bench: A Benchmark for Whole-Brain Activity Extraction in C. elegans

WormID-Bench evaluates methods for neuron detection, identification, and tracking in C. elegans using a multi-laboratory dataset, highlighting the need for improved computational approaches.

A connectomic study of a petascale fragment of human cerebral cortex

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

The paper develops an AI tool for quantifying ultrastructural traits from 3D microscopic datasets and improves fixation protocols for plants.

Vimo: Visual Analysis of Neuronal Connectivity Motifs

Vimo enables interactive visualization of neuronal connectivity motifs in large brain networks, allowing experts to sketch and analyze motif instances and chains in 3D.

Mapping Alzheimer Disease Molecular Pathologies in Large-Scale Connectomics Data: A Publicly Accessible Correlative Microscopy Resource

The paper presents a vCLEM protocol to map Aβ and pTau in AD mouse hippocampus connectomics data, revealing new molecular and structural alterations.

Saturated reconstruction of living brain tissue

This paper introduces a method for saturated reconstruction of living brain tissue using advanced imaging and deep learning to achieve high-resolution, volumetric analysis of neural structures over time.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex

The paper introduces a vCLEM method using fluorescent scFvs to combine molecular labeling with ssEM, revealing new cell types and subcellular features in mouse cerebellar cortex.

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

The paper introduces two methods to improve tissue segmentation in highly multiplexed images using real augmentations and nuclear envelope imaging, resulting in more accurate models.